CHANNEL_NAME stringclasses 1
value | URL stringlengths 43 43 | TITLE stringlengths 12 100 | DESCRIPTION stringlengths 66 5k | TRANSCRIPTION stringlengths 150 90.9k | SEGMENTS stringlengths 1.05k 146k |
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Yannic Kilcher | https://www.youtube.com/watch?v=G2sr1g6rLdE | Radioactive data: tracing through training (Paper Explained) | #ai #research #privacy
Data is the modern gold. Neural classifiers can improve their performance by training on more data, but given a trained classifier, it's difficult to tell what data it was trained on. This is especially relevant if you have proprietary or personal data and you want to make sure that other people... | Are you tired of other people training on your data? That annoys me every time it happens. I'm mad about this. If only there was a way to somehow mark your data and when other people train on it, their computer would explode. Well, this paper is a little bit like this, not entirely. The explosion part, I think they're... | [{"start": 0.0, "end": 4.8, "text": " Are you tired of other people training on your data?"}, {"start": 4.8, "end": 7.16, "text": " That annoys me every time it happens."}, {"start": 7.16, "end": 9.84, "text": " I'm mad about this."}, {"start": 9.84, "end": 15.8, "text": " If only there was a way to somehow mark your d... |
Yannic Kilcher | https://www.youtube.com/watch?v=9-o2aAoN0rY | Fast reinforcement learning with generalized policy updates (Paper Explained) | #ai #research #reinforcementlearning
Reinforcement Learning is a powerful tool, but it is also incredibly data-hungry. Given a new task, an RL agent has to learn a good policy entirely from scratch. This paper proposes a new framework that allows an agent to carry over knowledge from previous tasks into solving new ta... | Hi there! Today we're looking at fast reinforcement learning with generalized policy updates by André Barreto, Chavo Hall, Dianna Borsa, David Silver, and Dóinab Precu. So on high level this paper proposes a framework for reinforcement learning where you have many tasks at the same time and they propose framework wher... | [{"start": 0.0, "end": 4.32, "text": " Hi there! Today we're looking at fast reinforcement learning with generalized"}, {"start": 4.32, "end": 9.76, "text": " policy updates by Andr\u00e9 Barreto, Chavo Hall, Dianna Borsa, David Silver, and"}, {"start": 9.76, "end": 16.36, "text": " D\u00f3inab Precu. So on high level ... |
Yannic Kilcher | https://www.youtube.com/watch?v=a4VvcmqnkhY | What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study (Paper Explained) | #ai #research #machinelearning
Online Reinforcement Learning is a flourishing field with countless methods for practitioners to choose from. However, each of those methods comes with a plethora of hyperparameter choices. This paper builds a unified framework for five continuous control tasks and investigates in a larg... | Hello there. Today we're looking at what matters in on policy reinforcement learning, a large scale empirical study by Google Brain. On a high level, this paper investigates a five different continuous control tasks and they train agents with all the different choices that you can make basically on these continuous co... | [{"start": 0.0, "end": 10.0, "text": " Hello there. Today we're looking at what matters in on policy reinforcement learning, a large scale empirical study by Google Brain."}, {"start": 10.0, "end": 24.0, "text": " On a high level, this paper investigates a five different continuous control tasks and they train agents w... |
Yannic Kilcher | https://www.youtube.com/watch?v=VgqHitvEbR0 | [Rant] REVIEWER #2: How Peer Review is FAILING in Machine Learning | #ai #research #peerreview
Machine Learning research is in dire straits as more people flood into the field and competent reviewers are scarce and overloaded. This video takes a look at the incentive structures behind the current system and describes how they create a negative feedback loop. In the end, I'll go through... | It's review time, review time. So NURRIPS has recently released the reviews for submitted papers and pretty much everyone is not happy. And I think the reason is that even though you have the reasonable reviewers of these conferences, there is always, always reviewer number two. And reviewer number two leaves very sho... | [{"start": 0.0, "end": 6.6000000000000005, "text": " It's review time, review time."}, {"start": 6.6000000000000005, "end": 14.32, "text": " So NURRIPS has recently released the reviews for submitted papers and pretty much everyone"}, {"start": 14.32, "end": 16.36, "text": " is not happy."}, {"start": 16.36, "end": 22.... |
Yannic Kilcher | https://www.youtube.com/watch?v=lj-LGrnh1oU | REALM: Retrieval-Augmented Language Model Pre-Training (Paper Explained) | #ai #tech #science
Open Domain Question Answering is one of the most challenging tasks in NLP. When answering a question, the model is able to retrieve arbitrary documents from an indexed corpus to gather more information. REALM shows how Masked Language Modeling (MLM) pretraining can be used to train a retriever for ... | What's the angle of an equilateral triangle? So if your high school math isn't fresh in your head, you might be forgiven for not knowing this. But what do people do when they want to find out the answer to that question? Of course the standard way nowadays is to go to search engine like Google, type in the question, f... | [{"start": 0.0, "end": 5.54, "text": " What's the angle of an equilateral triangle? So if your high school math isn't"}, {"start": 5.54, "end": 10.540000000000001, "text": " fresh in your head, you might be forgiven for not knowing this. But what do people"}, {"start": 10.540000000000001, "end": 15.040000000000001, "te... |
Yannic Kilcher | https://www.youtube.com/watch?v=v2GRWzIhaqQ | Meta-Learning through Hebbian Plasticity in Random Networks (Paper Explained) | #ai #neuroscience #rl
Reinforcement Learning is a powerful tool, but it lacks biological plausibility because it learns a fixed policy network. Animals use neuroplasticity to reconfigure their policies on the fly and quickly adapt to new situations. This paper uses Hebbian Learning, a biologically inspired technique, ... | Hi there. Take a look at the following problem on the left right here. See, have this quadruped and the goal is to have it walk forward or in any direction as far as possible. Now usually this is the domain of sort of reinforcement learning. So you have inputs which is the sensors of the joints of the quadruped and yo... | [{"start": 0.0, "end": 5.28, "text": " Hi there. Take a look at the following problem on the left right here. See,"}, {"start": 5.28, "end": 10.4, "text": " have this quadruped and the goal is to have it walk forward or in any direction"}, {"start": 10.4, "end": 16.36, "text": " as far as possible. Now usually this is ... |
Yannic Kilcher | https://www.youtube.com/watch?v=nv6oFDp6rNQ | Hopfield Networks is All You Need (Paper Explained) | #ai #transformer #attention
Hopfield Networks are one of the classic models of biological memory networks. This paper generalizes modern Hopfield Networks to continuous states and shows that the corresponding update rule is equal to the attention mechanism used in modern Transformers. It further analyzes a pre-trained... | Hi there. Today we'll look at Hopfield Networks is all you need by researchers from the Yannis Kepler University in Lins and the University of Oslo. So on high level this paper proposes a new type of Hopfield Networks that generalizes modern Hopfield Networks from binary patterns to continuous patterns and then shows ... | [{"start": 0.0, "end": 5.72, "text": " Hi there. Today we'll look at Hopfield Networks is all you need by researchers from the"}, {"start": 5.72, "end": 12.68, "text": " Yannis Kepler University in Lins and the University of Oslo. So on high level this paper"}, {"start": 12.68, "end": 19.0, "text": " proposes a new typ... |
Yannic Kilcher | https://www.youtube.com/watch?v=udS2OPohs_s | I TRAINED AN AI TO SOLVE 2+2 (w/ Live Coding) | #ai #tech #code
A whole bunch of humans are arguing whether 2+2=4 or 2+2=5. Pointless! Let the machines handle this!
Colab: https://colab.research.google.com/drive/1tDjFW7CFGQG8vHdUAVNpr2EG9z0JZGYC?usp=sharing
Disclaimer: This is a joke.
Links:
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitt... | Hi there, you might have seen the recent debate about 2 plus 2, where everyone tries to weigh in. The big question being, is 2 plus 2 equal to 4, or is 2 plus 2 equal to 5? And for some reason, the entirety of Western civilization hangs in the balance right here. But everyone's missing the point. Everyone's just kind ... | [{"start": 0.0, "end": 5.6000000000000005, "text": " Hi there, you might have seen the recent debate about 2 plus 2, where everyone tries to weigh in."}, {"start": 5.6000000000000005, "end": 11.6, "text": " The big question being, is 2 plus 2 equal to 4, or is 2 plus 2 equal to 5?"}, {"start": 11.6, "end": 19.0, "text"... |
Yannic Kilcher | https://www.youtube.com/watch?v=ml3Y1ljVSQ8 | PCGRL: Procedural Content Generation via Reinforcement Learning (Paper Explained) | #ai #research #gaming
Deep RL is usually used to solve games, but this paper turns the process on its head and applies RL to game level creation. Compared to traditional approaches, it frames level design as a sequential decision making progress and ends up with a fast and diverse level generator.
OUTLINE:
0:00 - Int... | Hi there. Have you ever wondered how video game levels are made? Yeah, me neither. But this paper has. And in this paper you can see a reinforcement learning agent that has learned how to make video game levels in various ways. So this is implemented for this game here where the goal is simply to make the longest maze... | [{"start": 0.0, "end": 6.2, "text": " Hi there. Have you ever wondered how video game levels are made? Yeah, me neither."}, {"start": 6.2, "end": 12.36, "text": " But this paper has. And in this paper you can see a reinforcement learning agent"}, {"start": 12.36, "end": 19.080000000000002, "text": " that has learned ho... |
Yannic Kilcher | https://www.youtube.com/watch?v=WVPE62Gk3EM | Big Bird: Transformers for Longer Sequences (Paper Explained) | #ai #nlp #attention
The quadratic resource requirements of the attention mechanism are the main roadblock in scaling up transformers to long sequences. This paper replaces the full quadratic attention mechanism by a combination of random attention, window attention, and global attention. Not only does this allow the p... | Hi there. Today we'll look at Big Bird Transformers for longer sequences by Maniel Zayer and Guru Garuganesh at Al of Google Research. So this paper on a high-level proposes to replace the quadratic attention mechanism in transformers by a mix of random attention, windowed attention, and selective global attention. Th... | [{"start": 0.0, "end": 5.28, "text": " Hi there. Today we'll look at Big Bird Transformers for longer sequences by"}, {"start": 5.28, "end": 10.74, "text": " Maniel Zayer and Guru Garuganesh at Al of Google Research. So this paper on a"}, {"start": 10.74, "end": 14.72, "text": " high-level proposes to replace the quadr... |
Yannic Kilcher | https://www.youtube.com/watch?v=q7PjrmGNx5A | Self-training with Noisy Student improves ImageNet classification (Paper Explained) | The abundance of data on the internet is vast. Especially unlabeled images are plentiful and can be collected with ease. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. First, a teacher model is trained in a supervised fashion. Then, that teacher is used to lab... | Hi there, today we look at self-training with noisy student improves image net classification by Tidze Sier, Mintan Luang, Edward Havi and Quok Vile. So this paper takes an image net classifier that's been trained on the image net dataset and uses that classifier as a teacher model to label a whole bunch of unlabeled ... | [{"start": 0.0, "end": 4.6000000000000005, "text": " Hi there, today we look at self-training with noisy student improves image"}, {"start": 4.6000000000000005, "end": 12.0, "text": " net classification by Tidze Sier, Mintan Luang, Edward Havi and Quok Vile. So this"}, {"start": 12.0, "end": 16.6, "text": " paper takes... |
Yannic Kilcher | https://www.youtube.com/watch?v=rFwQDDbYTm4 | [Classic] Playing Atari with Deep Reinforcement Learning (Paper Explained) | #ai #dqn #deepmind
After the initial success of deep neural networks, especially convolutional neural networks on supervised image processing tasks, this paper was the first to demonstrate their applicability to reinforcement learning. Deep Q Networks learn from pixel input to play seven different Atari games and outp... | Hi there, today we'll look at playing Atari with deep reinforcement learning by Vladimir Mni at all of DeepMind. So this is another one of our series of impactful past papers. This paper right here kicked off an entire revolution in reinforcement learning. Specifically, it sort of started the deep reinforcement learni... | [{"start": 0.0, "end": 5.42, "text": " Hi there, today we'll look at playing Atari with deep reinforcement learning by Vladimir"}, {"start": 5.42, "end": 8.1, "text": " Mni at all of DeepMind."}, {"start": 8.1, "end": 13.52, "text": " So this is another one of our series of impactful past papers."}, {"start": 13.52, "e... |
Yannic Kilcher | https://www.youtube.com/watch?v=Nq3auVtvd9Q | [Classic] ImageNet Classification with Deep Convolutional Neural Networks (Paper Explained) | #ai #research #alexnet
AlexNet was the start of the deep learning revolution. Up until 2012, the best computer vision systems relied on hand-crafted features and highly specialized algorithms to perform object classification. This paper was the first to successfully train a deep convolutional neural network on not one... | Hi there, today we'll look at ImageNet classification with deep convolutional neural networks by Alex Krzyszewski, Ilya Satskyver and Jeffrey Hinton. So this paper is another one in the installment of our historical paper overview where we go through kind of old papers that were or weren't very impactful and see what ... | [{"start": 0.0, "end": 10.0, "text": " Hi there, today we'll look at ImageNet classification with deep convolutional neural networks by Alex Krzyszewski, Ilya Satskyver and Jeffrey Hinton."}, {"start": 10.0, "end": 27.0, "text": " So this paper is another one in the installment of our historical paper overview where we... |
Yannic Kilcher | https://www.youtube.com/watch?v=a6v92P0EbJc | Neural Architecture Search without Training (Paper Explained) | #ai #research #machinelearning
Neural Architecture Search is typically very slow and resource-intensive. A meta-controller has to train many hundreds or thousands of different models to find a suitable building plan. This paper proposes to use statistics of the Jacobian around data points to estimate the performance o... | Hi there. Today we're looking at neural architecture search without training by Joseph Miller, Jack Turner, Amastorkee and Elliott J. Crowley. On a high level this paper performs neural architecture search by looking at the correlation matrices of the Jacobian of the of the data when you pass it through the network an... | [{"start": 0.0, "end": 5.78, "text": " Hi there. Today we're looking at neural architecture search without training by Joseph"}, {"start": 5.78, "end": 12.24, "text": " Miller, Jack Turner, Amastorkee and Elliott J. Crowley. On a high level this paper"}, {"start": 12.24, "end": 18.44, "text": " performs neural architec... |
Yannic Kilcher | https://www.youtube.com/watch?v=eyxmSmjmNS0 | [Classic] Generative Adversarial Networks (Paper Explained) | #ai #deeplearning #gan
GANs are of the main models in modern deep learning. This is the paper that started it all! While the task of image classification was making progress, the task of image generation was still cumbersome and prone to artifacts. The main idea behind GANs is to pit two competing networks against eac... | Hi there, today we'll look at Generative Adversarial Nets by ENJ Goodfellow at all. So this one is another installment in our series of historical papers that had great impact. Gans nowadays, or Generative Adversarial Nets back then, were sort of, this was the starting shot in a long line of research that is still con... | [{"start": 0.0, "end": 7.12, "text": " Hi there, today we'll look at Generative Adversarial Nets by ENJ Goodfellow at all."}, {"start": 7.12, "end": 13.36, "text": " So this one is another installment in our series of historical papers that had great impact."}, {"start": 13.36, "end": 21.66, "text": " Gans nowadays, or... |
Yannic Kilcher | https://www.youtube.com/watch?v=yexR53My2O4 | [Classic] Word2Vec: Distributed Representations of Words and Phrases and their Compositionality | #ai #research #word2vec
Word vectors have been one of the most influential techniques in modern NLP to date. This paper describes Word2Vec, which the most popular technique to obtain word vectors. The paper introduces the negative sampling technique as an approximation to noise contrastive estimation and shows that th... | Hi there, today we'll look at distributed representations of words and phrases and their compositionality by Thomas Mikolov, Eliasotskyvur, Kai Chen, Greg Karato and Jeffrey Dean. This is another historical paper. It's one of three papers, it's the middle one that introduces the original word to vec algorithm. And if ... | [{"start": 0.0, "end": 5.6000000000000005, "text": " Hi there, today we'll look at distributed representations of words and phrases and their"}, {"start": 5.6000000000000005, "end": 12.64, "text": " compositionality by Thomas Mikolov, Eliasotskyvur, Kai Chen, Greg Karato and Jeffrey Dean."}, {"start": 12.64, "end": 18.... |
Yannic Kilcher | https://www.youtube.com/watch?v=GWt6Fu05voI | [Classic] Deep Residual Learning for Image Recognition (Paper Explained) | #ai #research #resnet
ResNets are one of the cornerstones of modern Computer Vision. Before their invention, people were not able to scale deep neural networks beyond 20 or so layers, but with this paper's invention of residual connections, all of a sudden networks could be arbitrarily deep. This led to a big spike in... | Hi there, today we'll look at deep residual learning for image recognition by Kaimin He, Xiang Yu Chang, Xiao Qing Ran and Jian Sun. So this, you know it, this is an old paper. It is from 2015, but I thought we'd still look at it because this not only is it one of the most influential papers in modern deep learning, i... | [{"start": 0.0, "end": 6.4, "text": " Hi there, today we'll look at deep residual learning for image recognition by Kaimin He,"}, {"start": 6.4, "end": 11.36, "text": " Xiang Yu Chang, Xiao Qing Ran and Jian Sun."}, {"start": 11.36, "end": 15.68, "text": " So this, you know it, this is an old paper."}, {"start": 15.68,... |
Yannic Kilcher | https://www.youtube.com/watch?v=GwItCHOifG8 | I'M TAKING A BREAK... (Channel Update July 2020) | Past, Present & Future of this Channel.
OUTLINE:
0:00 - I'm going on a break
0:20 - Channel Stats
1:20 - Other Platforms
4:20 - Drama Videos
5:30 - Flatland
8:40 - SpineNet Thumbnail
9:55 - Future Content
12:55 - How do I select papers?
15:50 - Financial Support, Ads & Merch
18:50 - Conclusion
Our Flatland Repo: http... | Yes, you're at that right. I am going on a break. Don't worry though. There will still be videos just not as many I've decided to basically reduce the upload frequency a little bit mostly because I am going on a break But also because I kind of want to have time to do other things But we'll get to that later. So how's... | [{"start": 0.0, "end": 7.24, "text": " Yes, you're at that right. I am going on a break. Don't worry though. There will still be videos just not as many"}, {"start": 7.24, "end": 14.34, "text": " I've decided to basically reduce the upload frequency a little bit mostly because I am going on a break"}, {"start": 14.34, ... |
Yannic Kilcher | https://www.youtube.com/watch?v=5IRlUVrEVL8 | Deep Ensembles: A Loss Landscape Perspective (Paper Explained) | #ai #research #optimization
Deep Ensembles work surprisingly well for improving the generalization capabilities of deep neural networks. Surprisingly, they outperform Bayesian Networks, which are - in theory - doing the same thing. This paper investigates how Deep Ensembles are especially suited to capturing the non-c... | Hi there, today we'll look at Deep Ensembles, a Los Landscape perspective by Stanislav Furt, Huihi, Hu and Balaji Lakshminarayanan. This paper on a high level explains the Los Landscape of Deep Ensembles models, so Ensembles of Deep Neural Network. And it hypothesizes and it shows through experiments that each member ... | [{"start": 0.0, "end": 5.92, "text": " Hi there, today we'll look at Deep Ensembles, a Los Landscape perspective by Stanislav Furt,"}, {"start": 5.92, "end": 12.040000000000001, "text": " Huihi, Hu and Balaji Lakshminarayanan. This paper on a high level explains the"}, {"start": 12.040000000000001, "end": 17.04, "text"... |
Yannic Kilcher | https://www.youtube.com/watch?v=v-ZxzTSpmk4 | Gradient Origin Networks (Paper Explained w/ Live Coding) | Neural networks for implicit representations, such as SIRENs, have been very successful at modeling natural signals. However, in the classical approach, each data point requires its own neural network to be fit. This paper extends implicit representations to an entire dataset by introducing latent vectors of data point... | Hi there, today we'll look at gradient origin networks by Sam Bond Taylor and Chris G. Wilcox of Durham University. So on a high level, this paper trains implicit representation networks, but not on single data points, but on entire data set. It does so by using a latent encoding of each data point. And it doesn't obt... | [{"start": 0.0, "end": 9.0, "text": " Hi there, today we'll look at gradient origin networks by Sam Bond Taylor and Chris G. Wilcox of Durham University."}, {"start": 9.0, "end": 17.0, "text": " So on a high level, this paper trains implicit representation networks, but not on single data points, but on entire data set... |
Yannic Kilcher | https://www.youtube.com/watch?v=x6T1zMSE4Ts | NVAE: A Deep Hierarchical Variational Autoencoder (Paper Explained) | VAEs have been traditionally hard to train at high resolutions and unstable when going deep with many layers. In addition, VAE samples are often more blurry and less crisp than those from GANs. This paper details all the engineering choices necessary to successfully train a deep hierarchical VAE that exhibits global co... | Alright, hi there. Have a look at these faces right here. So you're probably used by now to seeing computer generated faces of really high quality. But probably you're used to seeing these faces coming from a generative adversarial network. However, these faces right here are from a variational autoencoder. Now variat... | [{"start": 0.0, "end": 2.04, "text": " Alright, hi there."}, {"start": 2.04, "end": 4.08, "text": " Have a look at these faces right here."}, {"start": 4.08, "end": 8.92, "text": " So you're probably used by now to seeing computer generated faces of really high quality."}, {"start": 8.92, "end": 14.48, "text": " But pr... |
Yannic Kilcher | https://www.youtube.com/watch?v=Jqvb7jp4Nm8 | Addendum for Supermasks in Superposition: A Closer Look (Paper Explained) | I take a closer look at "Supermasks in Superposition" after I've already done a video on it. Specifically, I look at: 1. The intuition and theoretical justification behind the G objective, 2. Whether Supermasks and Superposition can be viewed as two distinct ideas and 3. The Paper's Broader Impact Statement.
OUTLINE:
... | Hi there. Today we'll look at super masks and super positioned again. So this is part two of this paper by Mitchell Wurzman and Vivek Ramonujin. And here's the reason why there's a part two. So after yesterday's video on this paper, I couldn't sleep because I really felt that I had left out some important aspects that... | [{"start": 0.0, "end": 9.0, "text": " Hi there. Today we'll look at super masks and super positioned again. So this is part two of this paper by Mitchell Wurzman and Vivek Ramonujin."}, {"start": 9.0, "end": 24.0, "text": " And here's the reason why there's a part two. So after yesterday's video on this paper, I couldn... |
Yannic Kilcher | https://www.youtube.com/watch?v=3jT1qJ8ETzk | SupSup: Supermasks in Superposition (Paper Explained) | Supermasks are binary masks of a randomly initialized neural network that result in the masked network performing well on a particular task. This paper considers the problem of (sequential) Lifelong Learning and trains one Supermask per Task, while keeping the randomly initialized base network constant. By minimizing t... | Hi there, today we'll look at supermasks in superposition by Mitchell Wurzmann, Vivek Ramanujin at L. So on a high level this paper tackles the problem of sequentially learning many many tasks without catastrophic forgetting by leveraging these things called supermasks. A supermask is basically a binary mask that you ... | [{"start": 0.0, "end": 5.76, "text": " Hi there, today we'll look at supermasks in superposition by Mitchell Wurzmann, Vivek"}, {"start": 5.76, "end": 11.76, "text": " Ramanujin at L. So on a high level this paper tackles the problem of sequentially"}, {"start": 11.76, "end": 17.32, "text": " learning many many tasks w... |
Yannic Kilcher | https://www.youtube.com/watch?v=z_3Qv4In2ac | [Live Machine Learning Research] Plain Self-Ensembles (I actually DISCOVER SOMETHING) - Part 1 | I share my progress of implementing a research idea from scratch. I attempt to build an ensemble model out of students of label-free self-distillation without any additional data or augmentation. Turns out, it actually works, and interestingly, the more students I employ, the better the accuracy. This leads to the hypo... | Hey what's up? So I've had this relatively dumb research idea and people have been asking me for more coding videos and so on so I thought why not do a video where I take a research idea and implement it from scratch just to show how one would go or how I would go about implementing something like this. Now this was s... | [{"start": 0.0, "end": 6.5600000000000005, "text": " Hey what's up? So I've had this relatively dumb research idea and people have been"}, {"start": 6.5600000000000005, "end": 11.120000000000001, "text": " asking me for more coding videos and so on so I thought why not do a video"}, {"start": 11.120000000000001, "end":... |
Yannic Kilcher | https://www.youtube.com/watch?v=qFRfnIRMNlk | SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization (Paper Explained) | #machinelearning #ai #google
The high-level architecture of CNNs has not really changed over the years. We tend to build high-resolution low-dimensional layers first, followed by ever more coarse, but deep layers. This paper challenges this decades-old heuristic and uses neural architecture search to find an alternati... | Hi there, today we'll look at SpineNet learning scale-per-muted backbone for recognition and localization by Xianze Du at all of Google research. On a high level this paper proposes to take current recognition and localization networks which have a CNN backbone, usually something like a ResNet, and switch up the order... | [{"start": 0.0, "end": 6.16, "text": " Hi there, today we'll look at SpineNet learning scale-per-muted backbone for recognition and"}, {"start": 6.16, "end": 10.8, "text": " localization by Xianze Du at all of Google research."}, {"start": 10.8, "end": 16.96, "text": " On a high level this paper proposes to take curren... |
Yannic Kilcher | https://www.youtube.com/watch?v=hAooAOFRsYc | Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention (Paper Explained) | #ai #attention #transformer #deeplearning
Transformers are famous for two things: Their superior performance and their insane requirements of compute and memory. This paper reformulates the attention mechanism in terms of kernel functions and obtains a linear formulation, which reduces these requirements. Surprisingly... | Hi there. Today we're looking at transformers or RNNs, fast-order-regressive transformers with linear attention by Angleau-Scataropoulos, Apaure-Vierch, Nicolas-Ous-Pappas, and François Fleuré. So this paper on a high level proposes to interpret the attention mechanism in transformers with a in terms of a kernel funct... | [{"start": 0.0, "end": 5.48, "text": " Hi there. Today we're looking at transformers or RNNs, fast-order-regressive"}, {"start": 5.48, "end": 9.48, "text": " transformers with linear attention by Angleau-Scataropoulos,"}, {"start": 9.48, "end": 15.4, "text": " Apaure-Vierch, Nicolas-Ous-Pappas, and Fran\u00e7ois Fleur\... |
Yannic Kilcher | https://www.youtube.com/watch?v=O9kFX33nUcU | On the Measure of Intelligence by François Chollet - Part 4: The ARC Challenge (Paper Explained) | In this part, we look at the ARC challenge as a proposed test of machine intelligence. The dataset features 1000 tasks that test rapid generalization based on human core knowledge priors, such as object-ness, symmetry, and navigation.
OUTLINE:
0:00 - Intro
0:55 - What is ARC?
6:30 - The Goals of ARC
10:40 - Assumed Pr... | Hi there and welcome to the last part of on the measure of intelligence by François Cholé. This last part concerns the arc challenge that Cholé has proposed or the arc data set, which stands for the abstraction and reasoning corpus. And we're just quickly going over the data set, look how it's built, and discuss what ... | [{"start": 0.0, "end": 9.0, "text": " Hi there and welcome to the last part of on the measure of intelligence by Fran\u00e7ois Chol\u00e9."}, {"start": 9.0, "end": 16.0, "text": " This last part concerns the arc challenge that Chol\u00e9 has proposed or the arc data set,"}, {"start": 16.0, "end": 20.0, "text": " which ... |
Yannic Kilcher | https://www.youtube.com/watch?v=q6Kyvy1zLwQ | BERTology Meets Biology: Interpreting Attention in Protein Language Models (Paper Explained) | Proteins are the workhorses of almost all cellular functions and a core component of life. But despite their versatility, all proteins are built as sequences of the same 20 amino acids. These sequences can be analyzed with tools from NLP. This paper investigates the attention mechanism of a BERT model that has been tra... | Hi there! Today we'll look at Berkthology meets Biology, interpreting attention in protein language models. By Jesse Vigg, Ali Madani, Lav R. Varshani, Kaimim Siong, Richard Soccer, and Niznian Fatima Rajani. This paper is an investigative paper into models that are trained on biological data specifically into Berkth ... | [{"start": 0.0, "end": 7.0, "text": " Hi there! Today we'll look at Berkthology meets Biology, interpreting attention in protein language models."}, {"start": 7.0, "end": 16.0, "text": " By Jesse Vigg, Ali Madani, Lav R. Varshani, Kaimim Siong, Richard Soccer, and Niznian Fatima Rajani."}, {"start": 16.0, "end": 26.0, ... |
Yannic Kilcher | https://www.youtube.com/watch?v=1VdEw_mGjFk | GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding (Paper Explained) | Google builds a 600 billion parameter transformer to do massively multilingual, massive machine translation. Interestingly, the larger model scale does not come from increasing depth of the transformer, but from increasing width in the feedforward layers, combined with a hard routing to parallelize computations on up t... | OpenAI has 175 billion parameter model. You thought that was large? That's cute. Check out Google's 600 billion parameter model. 600 billion floating point numbers doing things at the same time. This has absolutely become a body part measuring competitions between companies. Google be like, oh, GPT-3. I spit on you. I... | [{"start": 0.0, "end": 5.0, "text": " OpenAI has 175 billion parameter model."}, {"start": 5.0, "end": 7.0, "text": " You thought that was large?"}, {"start": 7.0, "end": 8.0, "text": " That's cute."}, {"start": 8.0, "end": 12.0, "text": " Check out Google's 600 billion parameter model."}, {"start": 12.0, "end": 17.0, ... |
Yannic Kilcher | https://www.youtube.com/watch?v=DYBmD88vpiA | Object-Centric Learning with Slot Attention (Paper Explained) | Visual scenes are often comprised of sets of independent objects. Yet, current vision models make no assumptions about the nature of the pictures they look at. By imposing an objectness prior, this paper a module that is able to recognize permutation-invariant sets of objects from pixels in both supervised and unsuperv... | Hi there, today we'll look at object-centric learning with slot attention by Francesco Locotello, Thomas Kip and others of Google Brain, ETH Zurich and MPI. On high level, this paper recognizes scenes of objects from single pixels and it's best I show you a picture of what's going on. So you have scenes like this wher... | [{"start": 0.0, "end": 5.44, "text": " Hi there, today we'll look at object-centric learning with slot attention by Francesco"}, {"start": 5.44, "end": 11.44, "text": " Locotello, Thomas Kip and others of Google Brain, ETH Zurich and MPI."}, {"start": 11.44, "end": 17.32, "text": " On high level, this paper recognizes ... |
Yannic Kilcher | https://www.youtube.com/watch?v=V79rRI05Lj4 | Set Distribution Networks: a Generative Model for Sets of Images (Paper Explained) | We've become very good at making generative models for images and classes of images, but not yet of sets of images, especially when the number of sets is unknown and can contain sets that have never been encountered during training. This paper builds a probabilistic framework and a practical implementation of a generat... | Hi there. Today we're looking at set distribution networks, a generative model for sets of images by Schwungfeich Chai, Walter Tablet, Miguel Angel Bautista, Carlos Gastron, and Josh M. Suskind of Apple. So this paper introduces a generative model for sets, and it does so in an energy-based model fashion. It will have... | [{"start": 0.0, "end": 6.48, "text": " Hi there. Today we're looking at set distribution networks, a generative model for sets of images by"}, {"start": 6.48, "end": 13.76, "text": " Schwungfeich Chai, Walter Tablet, Miguel Angel Bautista, Carlos Gastron, and Josh M. Suskind of Apple."}, {"start": 13.76, "end": 21.84, ... |
Yannic Kilcher | https://www.youtube.com/watch?v=eI8xTdcZ6VY | Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection (Paper Explained) | Object detection often does not occur in a vacuum. Static cameras, such as wildlife traps, collect lots of irregularly sampled data over a large time frame and often capture repeating or similar events. This model learns to dynamically incorporate other frames taken by the same camera into its object detection pipeline... | Hi there, today we'll look at context R-CNN long-term temporal context for per camera object detection by Sarah Beery, Guan Huang Wu, Vivek Rathad, Ronnie Votel and Jonathan Huang. So on a high level this paper tries to do object detection for cameras where the camera is in the same place for a long time. For example ... | [{"start": 0.0, "end": 6.74, "text": " Hi there, today we'll look at context R-CNN long-term temporal context for per camera object"}, {"start": 6.74, "end": 13.4, "text": " detection by Sarah Beery, Guan Huang Wu, Vivek Rathad, Ronnie Votel and Jonathan Huang."}, {"start": 13.4, "end": 19.96, "text": " So on a high le... |
Yannic Kilcher | https://www.youtube.com/watch?v=Hdo81GtLC_4 | Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures (Paper Explained) | Backpropagation is one of the central components of modern deep learning. However, it's not biologically plausible, which limits the applicability of deep learning to understand how the human brain works. Direct Feedback Alignment is a biologically plausible alternative and this paper shows that, contrary to previous r... | Hi there! Today we'll look at direct feedback alignment scales to modern deep learning tasks and architectures by Julia Lone, Jakob Opoli, François Bonifass and Floron Crizacala. So this paper on a high level it replaces the back propagation algorithm in deep learning architectures with this algorithm called direct fe... | [{"start": 0.0, "end": 4.72, "text": " Hi there! Today we'll look at direct feedback alignment scales to modern"}, {"start": 4.72, "end": 10.72, "text": " deep learning tasks and architectures by Julia Lone, Jakob Opoli, Fran\u00e7ois Bonifass"}, {"start": 10.72, "end": 16.92, "text": " and Floron Crizacala. So this pa... |
Yannic Kilcher | https://www.youtube.com/watch?v=cuyM63ugsxI | On the Measure of Intelligence by François Chollet - Part 3: The Math (Paper Explained) | In this part, we go over the formal definition of the measure of intelligence. In order to do this, we have to frame and quantify the notions of generalization difficulty, priors, and experience in terms of algorithmic complexity.
OUTLINE:
0:00 - Intro & Recap
2:50 - Concept Schema
10:00 - Algorithmic Complexity
13:00... | Hello and welcome to the third part on the measure of intelligence by François Cholé. Now this is a multi-part series if you haven't seen the first two parts I recommend to watch at least one of them. There's somewhat overlapping but we've basically gone over the history of intelligence measurement and the foundations... | [{"start": 0.0, "end": 7.0, "text": " Hello and welcome to the third part on the measure of intelligence by Fran\u00e7ois Chol\u00e9."}, {"start": 7.0, "end": 14.0, "text": " Now this is a multi-part series if you haven't seen the first two parts I recommend to watch at least one of them."}, {"start": 14.0, "end": 28.0... |
Yannic Kilcher | https://www.youtube.com/watch?v=LMb5tvW-UoQ | Discovering Symbolic Models from Deep Learning with Inductive Biases (Paper Explained) | Neural networks are very good at predicting systems' numerical outputs, but not very good at deriving the discrete symbolic equations that govern many physical systems. This paper combines Graph Networks with symbolic regression and shows that the strong inductive biases of these models can be used to derive accurate s... | Hi there. Today we're looking at discovering symbolic models from deep learning with inductive biases by Miles Kranmer, Alvaro Sunchez Gonzales, Peter Pitalia, Ruizu, Kyle Kranmer, David Spurgel, and Shirley Ho. So this paper on a high level, it uses graph neural networks to fit a data set of observations of a physica... | [{"start": 0.0, "end": 7.0200000000000005, "text": " Hi there. Today we're looking at discovering symbolic models from deep learning with inductive biases by Miles Kranmer,"}, {"start": 7.0200000000000005, "end": 13.86, "text": " Alvaro Sunchez Gonzales, Peter Pitalia, Ruizu, Kyle Kranmer, David Spurgel, and Shirley Ho... |
Yannic Kilcher | https://www.youtube.com/watch?v=Uumd2zOOz60 | How I Read a Paper: Facebook's DETR (Video Tutorial) | I retrace my first reading of Facebook AI's DETR paper and explain my process of understanding it.
OUTLINE:
0:00 - Introduction
1:25 - Title
4:10 - Authors
5:55 - Affiliation
7:40 - Abstract
13:50 - Pictures
20:30 - Introduction
22:00 - Related Work
24:00 - Model
30:00 - Experiments
41:50 - Conclusions & Abstract
42:4... | Hi there people, so a lot of you have asked me how I read papers and honestly I don't think there is any super special method to it but you know I've I thought because people have asked me to make a video on it so I'll make a video on it and I'll try to share my method of reading papers and hopefully this is going to ... | [{"start": 0.0, "end": 8.0, "text": " Hi there people, so a lot of you have asked me how I read papers and honestly I"}, {"start": 8.0, "end": 13.040000000000001, "text": " don't think there is any super special method to it but you know I've I thought"}, {"start": 13.040000000000001, "end": 17.64, "text": " because pe... |
Yannic Kilcher | https://www.youtube.com/watch?v=qSArFEIoSbo | RepNet: Counting Out Time - Class Agnostic Video Repetition Counting in the Wild (Paper Explained) | Counting repeated actions in a video is one of the easiest tasks for humans, yet remains incredibly hard for machines. RepNet achieves state-of-the-art by creating an information bottleneck in the form of a temporal self-similarity matrix, relating video frames to each other in a way that forces the model to surface th... | Hi there, check out these videos on the top. Each one kind of contains a repeating action. So on the left you see someone doing a jumping jacks in a fairly regular pattern. In the middle it gets a bit more difficult because what you see is a tennis ball bouncing and it bounces faster and faster and faster as time goes... | [{"start": 0.0, "end": 4.44, "text": " Hi there, check out these videos on the top."}, {"start": 4.44, "end": 7.78, "text": " Each one kind of contains a repeating action."}, {"start": 7.78, "end": 12.9, "text": " So on the left you see someone doing a jumping jacks in a fairly regular pattern."}, {"start": 12.9, "end"... |
Yannic Kilcher | https://www.youtube.com/watch?v=n1SXlK5rhR8 | [Drama] Yann LeCun against Twitter on Dataset Bias | Yann LeCun points out an instance of dataset bias and proposes a sensible solution. People are not happy about it.
Original Tweet: https://twitter.com/ylecun/status/1274782757907030016
ERRATA:
- My specific example of the L1 regularizer wrt to Porsches and Ferraris does not actually work in this particular case. What... | Hi there, so you may have seen this already. There's a CVPR paper called Pulse and what it does is it's a method to up sample a pixelated image in a way that makes it look realistic but also that the again down sampled variant matches the original down sampled image. So it's kind of a cycle consistency loss together w... | [{"start": 0.0, "end": 7.84, "text": " Hi there, so you may have seen this already. There's a CVPR paper called Pulse and what it does is it's a method to"}, {"start": 8.2, "end": 9.32, "text": " up sample a"}, {"start": 9.32, "end": 15.32, "text": " pixelated image in a way that makes it look realistic but also that t... |
Yannic Kilcher | https://www.youtube.com/watch?v=Q5g3p9Zwjrk | SIREN: Implicit Neural Representations with Periodic Activation Functions (Paper Explained) | Implicit neural representations are created when a neural network is used to represent a signal as a function. SIRENs are a particular type of INR that can be applied to a variety of signals, such as images, sound, or 3D shapes. This is an interesting departure from regular machine learning and required me to think dif... | Hi there. Today we're looking at implicit neural representations with periodic activation functions by Vincent Sitzmann, Julian N. P. Martell, Alexander W. Bergman, David Bielandell, and Gordon Weststein. So this paper is a bit of a special paper. If you're like me coming from like classic machine learning or deep lea... | [{"start": 0.0, "end": 7.04, "text": " Hi there. Today we're looking at implicit neural representations with periodic activation functions"}, {"start": 7.04, "end": 13.52, "text": " by Vincent Sitzmann, Julian N. P. Martell, Alexander W. Bergman, David Bielandell, and Gordon"}, {"start": 13.52, "end": 20.8, "text": " W... |
Yannic Kilcher | https://www.youtube.com/watch?v=2lkUNDZld-4 | Big Self-Supervised Models are Strong Semi-Supervised Learners (Paper Explained) | This paper proposes SimCLRv2 and shows that semi-supervised learning benefits a lot from self-supervised pre-training. And stunningly, that effect gets larger the fewer labels are available and the more parameters the model has.
OUTLINE:
0:00 - Intro & Overview
1:40 - Semi-Supervised Learning
3:50 - Pre-Training via S... | Hi there. Today we'll look at big self-supervised models, our strong semi-supervised learners by Ting Chen, Simon Cornblath, Kevin Swarski, Moamut Nerozzi and Jeffrey Hinton of Google Brain. So this paper on a high level, it's also known as Sinclair V2, demonstrates that if you want to do semi-supervised learning, tha... | [{"start": 0.0, "end": 6.84, "text": " Hi there. Today we'll look at big self-supervised models, our strong semi-supervised learners"}, {"start": 6.84, "end": 13.16, "text": " by Ting Chen, Simon Cornblath, Kevin Swarski, Moamut Nerozzi and Jeffrey Hinton of Google"}, {"start": 13.16, "end": 20.96, "text": " Brain. So ... |
Yannic Kilcher | https://www.youtube.com/watch?v=THcuTJbeD34 | On the Measure of Intelligence by François Chollet - Part 2: Human Priors (Paper Explained) | In this part, we go much more in-depth into the relationship between intelligence, generality, skill, experience, and prior knowledge and take a close look at what priors are built into humans. This will form the basis for comparing the intelligence of humans and AI systems.
OUTLINE:
0:00 - Intro & Recap
3:00 - Optimi... | Hi there, today we're going to continue with on the measure of intelligence by François Cholet. Now, if you remember last time, if you haven't seen last time, go watch part one if you're interested. This is a multi-part series on this paper. Why? Because the paper itself is very long. It's 40 pages, the main part, and... | [{"start": 0.0, "end": 5.74, "text": " Hi there, today we're going to continue with on the measure of intelligence by Fran\u00e7ois"}, {"start": 5.74, "end": 6.74, "text": " Cholet."}, {"start": 6.74, "end": 12.02, "text": " Now, if you remember last time, if you haven't seen last time, go watch part one if you're"}, {... |
Yannic Kilcher | https://www.youtube.com/watch?v=YBlNQK0Ao6g | Image GPT: Generative Pretraining from Pixels (Paper Explained) | BERT and GPT-2/3 have shown the enormous power of using generative models as pre-training for classification tasks. However, for images, pre-training is usually done with supervised or self-supervised objectives. This paper investigates how far you can get when applying the principles from the world of NLP to the world... | Okay, I'm sure many of you have already seen this because it was rather widely announced, but the OpenAI team has announced a new model that produces pictures instead of text. So as you can see right here, on the left you'll always see like a half a picture. And on the right is the ground truth. So they took this pict... | [{"start": 0.0, "end": 6.72, "text": " Okay, I'm sure many of you have already seen this because it was rather widely announced,"}, {"start": 6.72, "end": 15.32, "text": " but the OpenAI team has announced a new model that produces pictures instead of text."}, {"start": 15.32, "end": 21.52, "text": " So as you can see ... |
Yannic Kilcher | https://www.youtube.com/watch?v=YPfUiOMYOEE | BYOL: Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (Paper Explained) | Self-supervised representation learning relies on negative samples to keep the encoder from collapsing to trivial solutions. However, this paper shows that negative samples, which are a nuisance to implement, are not necessary for learning good representation, and their algorithm BYOL is able to outperform other baseli... | Hello there. Today we're looking at Bootstrap your own latent, a new approach to self-supervised learning by researchers of DeepMind and Imperial College. So, almost no day goes by where we don't hear some sort of new self-supervised algorithm right here. This paper on a high level tries to get rid of the necessary ne... | [{"start": 0.0, "end": 6.0, "text": " Hello there. Today we're looking at Bootstrap your own latent, a new approach to self-supervised"}, {"start": 6.0, "end": 14.92, "text": " learning by researchers of DeepMind and Imperial College. So, almost no day goes by where"}, {"start": 14.92, "end": 21.080000000000002, "text"... |
Yannic Kilcher | https://www.youtube.com/watch?v=sEG8hD64c_Q | TUNIT: Rethinking the Truly Unsupervised Image-to-Image Translation (Paper Explained) | Image-to-Image translation usually requires corresponding samples or at least domain labels of the dataset. This paper removes that restriction and allows for fully unsupervised image translation of a source image to the style of one or many reference images. This is achieved by jointly training a guiding network that ... | Hi there. Today we'll look at rethinking the truly unsupervised image to image translation by Kyung-joon-e Baik, Yoon-jae Choi, Yong-jong-u, Ja-joon-u and Jung-jong-shim. So in this paper, we'll deal with image to image translation in an unsupervised fashion. So on a high level, they replace the need for domain or rea... | [{"start": 0.0, "end": 6.6000000000000005, "text": " Hi there. Today we'll look at rethinking the truly unsupervised image to image translation"}, {"start": 6.6000000000000005, "end": 16.84, "text": " by Kyung-joon-e Baik, Yoon-jae Choi, Yong-jong-u, Ja-joon-u and Jung-jong-shim."}, {"start": 16.84, "end": 23.72, "text... |
Yannic Kilcher | https://www.youtube.com/watch?v=DLq1DUcMh1Q | A bio-inspired bistable recurrent cell allows for long-lasting memory (Paper Explained) | Even though LSTMs and GRUs solve the vanishing and exploding gradient problems, they have trouble learning to remember things over very long time spans. Inspired from bistability, a property of biological neurons, this paper constructs a recurrent cell with an inherent memory property, with only minimal modification to... | Hi there! Today we're looking at a bio-inspired by-stable recurrent cell allows for long-lasting memory by Nicolas Vecovan, Damien Ernst, and Jean-Driandreon of the University of Liesch. This paper here is not a paper that wants to push state-of-the-art on anything. It is a paper that takes a concept from the biologic... | [{"start": 0.0, "end": 5.28, "text": " Hi there! Today we're looking at a bio-inspired by-stable recurrent cell"}, {"start": 5.28, "end": 11.68, "text": " allows for long-lasting memory by Nicolas Vecovan, Damien Ernst, and Jean-Driandreon"}, {"start": 11.68, "end": 17.080000000000002, "text": " of the University of Li... |
Yannic Kilcher | https://www.youtube.com/watch?v=8l-TDqpoUQs | SynFlow: Pruning neural networks without any data by iteratively conserving synaptic flow | The Lottery Ticket Hypothesis has shown that it's theoretically possible to prune a neural network at the beginning of training and still achieve good performance, if we only knew which weights to prune away. This paper does not only explain where other attempts at pruning fail, but provides an algorithm that provably ... | Hi there! Today we're looking at pruning neural networks without any data by iteratively conserving synaptic flow, by hiddenore tonaka, daniel kuneen, daniel lk, yummins, and suria gangouli. So this paper on a high level does what the lottery ticket hypothesis does, but does so without any data it prunes a neural netw... | [{"start": 0.0, "end": 4.96, "text": " Hi there! Today we're looking at pruning neural networks without any data by"}, {"start": 4.96, "end": 10.84, "text": " iteratively conserving synaptic flow, by hiddenore tonaka, daniel kuneen, daniel"}, {"start": 10.84, "end": 16.84, "text": " lk, yummins, and suria gangouli. So ... |
Yannic Kilcher | https://www.youtube.com/watch?v=l12GXD0t_RE | Deep Differential System Stability - Learning advanced computations from examples (Paper Explained) | Determining the stability properties of differential systems is a challenging task that involves very advanced symbolic and numeric mathematical manipulations. This paper shows that given enough training data, a simple language model with no underlying knowledge of mathematics can learn to solve these problems with rem... | Hi, here's a question for the WizKids among you. Is this system here controllable at a point xe with asymptotic control ue? I'll give you 10 seconds. Okay, 10 seconds are over. So to solve this, it's actually pretty easy. All you need to do is first differentiate the system with respect to its internal variables, whic... | [{"start": 0.0, "end": 4.64, "text": " Hi, here's a question for the WizKids among you."}, {"start": 4.64, "end": 11.16, "text": " Is this system here controllable at a point xe with asymptotic control ue?"}, {"start": 11.16, "end": 13.84, "text": " I'll give you 10 seconds."}, {"start": 13.84, "end": 15.32, "text": " ... |
Yannic Kilcher | https://www.youtube.com/watch?v=ZfDZRX3WiJg | VirTex: Learning Visual Representations from Textual Annotations (Paper Explained) | Pre-training a CNN backbone for visual transfer learning has recently seen a big push into the direction of incorporating more data, at the cost of less supervision. This paper investigates the opposite: Visual transfer learning by pre-training from very few, but very high-quality samples on an image captioning task.
... | Hi there. Today we're looking at vertex learning visual representations from textual annotations by Karen Desai and Justin Johnson of the University of Michigan. So this paper at its core is pretty simple. On high level it proposes to take the task of image captioning which is where you're given an image and you're as... | [{"start": 0.0, "end": 4.8, "text": " Hi there. Today we're looking at vertex learning visual representations from"}, {"start": 4.8, "end": 9.68, "text": " textual annotations by Karen Desai and Justin Johnson of the University of"}, {"start": 9.68, "end": 15.56, "text": " Michigan. So this paper at its core is pretty ... |
Yannic Kilcher | https://www.youtube.com/watch?v=-_2AF9Lhweo | Linformer: Self-Attention with Linear Complexity (Paper Explained) | Transformers are notoriously resource-intensive because their self-attention mechanism requires a squared number of memory and computations in the length of the input sequence. The Linformer Model gets around that by using the fact that often, the actual information in the attention matrix is of lower rank and can be a... | Hi there. Today we're going to look at Lin former self-attention with linear complexity by Sinon Wang, Belinda Li, Madyan, Kapsa, Han Fang and Haoma of Facebook AI. So on a high level, this paper observes that often the way we build transformers, the self-attention matrix is low rank and can be approximated by first p... | [{"start": 0.0, "end": 6.12, "text": " Hi there. Today we're going to look at Lin former self-attention with linear complexity"}, {"start": 6.12, "end": 14.280000000000001, "text": " by Sinon Wang, Belinda Li, Madyan, Kapsa, Han Fang and Haoma of Facebook AI. So on a high"}, {"start": 14.280000000000001, "end": 20.64, ... |
Yannic Kilcher | https://www.youtube.com/watch?v=WTB2p4bqtXU | End-to-End Adversarial Text-to-Speech (Paper Explained) | Text-to-speech engines are usually multi-stage pipelines that transform the signal into many intermediate representations and require supervision at each step. When trying to train TTS end-to-end, the alignment problem arises: Which text corresponds to which piece of sound? This paper uses an alignment module to tackle... | In this work, we take on the challenging task of learning to synthesize speech from normalized text or phonemes in an end-to-end manner, resulting in models which operate directly on character or phoneme input sequences and produce raw speech audio outputs. Okay, that wasn't the real model. I just thought it sounded r... | [{"start": 0.0, "end": 5.94, "text": " In this work, we take on the challenging task of learning to synthesize speech from"}, {"start": 5.94, "end": 11.040000000000001, "text": " normalized text or phonemes in an end-to-end manner, resulting in models which operate directly"}, {"start": 11.040000000000001, "end": 16.08... |
Yannic Kilcher | https://www.youtube.com/watch?v=xTzFJIknh7E | TransCoder: Unsupervised Translation of Programming Languages (Paper Explained) | Code migration between languages is an expensive and laborious task. To translate from one language to the other, one needs to be an expert at both. Current automatic tools often produce illegible and complicated code. This paper applies unsupervised neural machine translation to source code of Python, C++, and Java an... | Hi there. So the paper we're looking at today can take the code on the left, which is written in Python, and can output the code on the right, which is written in C++. Now the point here is that the code on the right does the same thing as the code on the left. So it is implementing the same function. The surprising t... | [{"start": 0.0, "end": 12.0, "text": " Hi there. So the paper we're looking at today can take the code on the left, which is written in Python, and can output the code on the right, which is written in C++."}, {"start": 12.0, "end": 20.0, "text": " Now the point here is that the code on the right does the same thing as... |
Yannic Kilcher | https://www.youtube.com/watch?v=cvkeWwDQr0A | JOIN ME for the NeurIPS 2020 Flatland Multi-Agent RL Challenge! | Join me to solve the NeurIPS 2020 challenge on multi-agent reinforcement learning in the flatland environment. This challenge has participants optimize a complex train scheduling system, subject to accidents, delays and re-routing. The plan is to solve this as a community with no expectations of winning and fully in th... | Hi there. Today I want to talk to you about something that's very near and dear to my heart and that is the flatland Environment now the flatland environment is a train simulator that has been developed by the Swiss train company And I write the trains every day So when I heard that there is a new rips challenge to us... | [{"start": 0.0, "end": 6.5600000000000005, "text": " Hi there. Today I want to talk to you about something that's very near and dear to my heart and that is the flatland"}, {"start": 7.08, "end": 13.44, "text": " Environment now the flatland environment is a train simulator that has been developed by the Swiss train co... |
Yannic Kilcher | https://www.youtube.com/watch?v=rl4nUngiR2k | BLEURT: Learning Robust Metrics for Text Generation (Paper Explained) | Proper evaluation of text generation models, such as machine translation systems, requires expensive and slow human assessment. As these models have gotten better in previous years, proxy-scores, like BLEU, are becoming less and less useful. This paper proposes to learn a proxy score and demonstrates that it correlates... | Hello there. Today we'll look at Blurt learning robust metrics for text generation by T-Balt Salam, T-Panjan Das and Ankur P-Parik. So this paper on a high level proposes a new metric for text generation tasks such as machine translation by leveraging a BERT model to produce like an automated metric, an automated qual... | [{"start": 0.0, "end": 6.640000000000001, "text": " Hello there. Today we'll look at Blurt learning robust metrics for text generation by T-Balt"}, {"start": 6.640000000000001, "end": 13.64, "text": " Salam, T-Panjan Das and Ankur P-Parik. So this paper on a high level proposes a new"}, {"start": 13.64, "end": 20.68, "... |
Yannic Kilcher | https://www.youtube.com/watch?v=4GKCxJQSw-g | Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search (Paper Explained) | Neural Architecture Search is usually prohibitively expensive in both time and resources to be useful. A search strategy has to keep evaluating new models, training them to convergence in an inner loop to find out if they are any good. This paper proposes to abstract the problem and extract the essential part of the ar... | Hi there! Today we're looking at synthetic petri dish, a novel surrogate model for rapid architecture search by Adi Tarawol, Joel Lehman, Philippe Petrovsky, Such, Jeff Kloon and Kenneth O. Stanley. This paper on a high level, it basically says if you want to do neural architecture search, if you for example search fo... | [{"start": 0.0, "end": 5.32, "text": " Hi there! Today we're looking at synthetic petri dish, a novel surrogate model for rapid"}, {"start": 5.32, "end": 12.0, "text": " architecture search by Adi Tarawol, Joel Lehman, Philippe Petrovsky, Such, Jeff Kloon and"}, {"start": 12.0, "end": 19.1, "text": " Kenneth O. Stanley... |
Yannic Kilcher | https://www.youtube.com/watch?v=CA8JPbJ75tY | CornerNet: Detecting Objects as Paired Keypoints (Paper Explained) | Many object detectors focus on locating the center of the object they want to find. However, this leaves them with the secondary problem of determining the specifications of the bounding box, leading to undesirable solutions like anchor boxes. This paper directly detects the top left and the bottom right corners of obj... | Hello there, today we're looking at corner net detecting objects as paired key points by high law and jihadeng. So on a high level, this paper detects objects in images. Let's say this is an image and here's a chair. You have your chair. And the way you detect the chair for this paper is going to be you detect the bot... | [{"start": 0.0, "end": 6.0, "text": " Hello there, today we're looking at corner net detecting objects as paired key points"}, {"start": 6.0, "end": 9.120000000000001, "text": " by high law and jihadeng."}, {"start": 9.120000000000001, "end": 13.92, "text": " So on a high level, this paper detects objects in images."},... |
Yannic Kilcher | https://www.youtube.com/watch?v=nxEr4VNgYOE | Movement Pruning: Adaptive Sparsity by Fine-Tuning (Paper Explained) | Deep neural networks are large models and pruning has become an important part of ML product pipelines, making models small while keeping their performance high. However, the classic pruning method, Magnitude Pruning, is suboptimal in models that are obtained by transfer learning. This paper proposes a solution, called... | Hi there, today we're looking at movement pruning, adaptive sparsity by fine tuning, by Victor Sun, Thomas Wolf, and Alexander M. Rush of hugging face and Cornell University. On a high level, this paper proposes that you should, if you have a transfer learning objective and you want to do pruning, you should not do pr... | [{"start": 0.0, "end": 6.16, "text": " Hi there, today we're looking at movement pruning, adaptive sparsity by fine tuning,"}, {"start": 6.16, "end": 13.16, "text": " by Victor Sun, Thomas Wolf, and Alexander M. Rush of hugging face and Cornell University."}, {"start": 13.16, "end": 17.92, "text": " On a high level, th... |
Yannic Kilcher | https://www.youtube.com/watch?v=hQEnzdLkPj4 | Learning To Classify Images Without Labels (Paper Explained) | How do you learn labels without labels? How do you classify images when you don't know what to classify them into? This paper investigates a new combination of representation learning, clustering, and self-labeling in order to group visually similar images together - and achieves surprisingly high accuracy on benchmark... | Hi there, check out these clusters of images right here. And just have a look at how all of them are pretty much showing the same objects. So here's balloons, here's birds, here's sharks or other fish. These are from images from the ImageNet data set. And you can see that these clusters are pretty much the object clas... | [{"start": 0.0, "end": 5.16, "text": " Hi there, check out these clusters of images right here."}, {"start": 5.16, "end": 10.120000000000001, "text": " And just have a look at how all of them are pretty much showing the same objects."}, {"start": 10.120000000000001, "end": 15.72, "text": " So here's balloons, here's bi... |
Yannic Kilcher | https://www.youtube.com/watch?v=3_qGrmD6iQY | On the Measure of Intelligence by François Chollet - Part 1: Foundations (Paper Explained) | How does one measure the Intelligence of an AI? Is AlphaGo intelligent? How about GPT-3? In this landmark paper, Chollet proposes a solid measure of intelligence for AI that revolves around generalization, rather than skill.
OUTLINE:
0:00 - Intro
1:15 - The need for a measure of intelligence
3:35 - Intelligence as gen... | Hello there. Today we're going to look at on the measure of intelligence by Honsuashou Lei of Google. This is a bit of a special episode, I would say, because if you look at the paper, it is, first of all, it's very long and then second of all, it is a wall of text, basically. Now, it's very interesting text, but if I... | [{"start": 0.0, "end": 6.32, "text": " Hello there. Today we're going to look at on the measure of intelligence by Honsuashou"}, {"start": 6.32, "end": 14.48, "text": " Lei of Google. This is a bit of a special episode, I would say, because if you look at the"}, {"start": 14.48, "end": 20.84, "text": " paper, it is, fi... |
Yannic Kilcher | https://www.youtube.com/watch?v=HYEzHX6-fIA | Dynamics-Aware Unsupervised Discovery of Skills (Paper Explained) | This RL framework can discover low-level skills all by itself without any reward. Even better, at test time it can compose its learned skills and reach a specified goal without any additional learning! Warning: Math-heavy!
OUTLINE:
0:00 - Motivation
2:15 - High-Level Overview
3:20 - Model-Based vs Model-Free Reinforce... | Hi there. Take a look at this humanoid right here. It walks from one checkpoint to another checkpoint and then to the next checkpoint and so on. And that is its task. It gets a reward from walking from checkpoint to checkpoint. Take a look at this end. This is called the end. It also walks from checkpoint to checkpoin... | [{"start": 0.0, "end": 2.72, "text": " Hi there."}, {"start": 2.72, "end": 5.32, "text": " Take a look at this humanoid right here."}, {"start": 5.32, "end": 12.040000000000001, "text": " It walks from one checkpoint to another checkpoint and then to the next checkpoint and so on."}, {"start": 12.040000000000001, "end"... |
Yannic Kilcher | https://www.youtube.com/watch?v=q7QP_lfqnQM | Synthesizer: Rethinking Self-Attention in Transformer Models (Paper Explained) | Do we really need dot-product attention? The attention mechanism is a central part of modern Transformers, mainly due to the dot-product attention mechanism. This paper changes the mechanism to remove the quadratic interaction terms and comes up with a new model, the Synthesizer. As it turns out, you can do pretty well... | Hi there. Today we're looking at synthesizer rethinking self-attention in transformer models by Yi Tai, Darabari, Donald Metzler, Dacheng, Chuan, Chia Zhao, and Chie Cheng. These people are of Google research and on a high level they're trying to replace the self-attention mechanism which is currently a dot product me... | [{"start": 0.0, "end": 5.78, "text": " Hi there. Today we're looking at synthesizer rethinking self-attention in"}, {"start": 5.78, "end": 13.36, "text": " transformer models by Yi Tai, Darabari, Donald Metzler, Dacheng, Chuan, Chia Zhao, and"}, {"start": 13.36, "end": 18.12, "text": " Chie Cheng. These people are of G... |
Yannic Kilcher | https://www.youtube.com/watch?v=LfUsGv-ESbc | [Code] How to use Facebook's DETR object detection algorithm in Python (Full Tutorial) | Watch my as I struggle my way up the glorious path of using the DETR object detection model in PyTorch.
Original Video on DETR: https://youtu.be/T35ba_VXkMY
Their GitHub repo: https://github.com/facebookresearch/detr
My Colab: https://colab.research.google.com/drive/1Exoc3-A141_h8GKk-B6cJxoidJsgOZOZ?usp=sharing
OUTL... | How the how's it going? So today we are going to try out the DETR, the end-to-end object detection with transformers from Facebook AI research and they have a GitHub repo and they pretty much give you everything like the model, the pre-trained weight, and so on. So today we're going to check out how easy it is to get ... | [{"start": 0.0, "end": 7.96, "text": " How the how's it going? So today we are going to try out the DETR, the end-to-end"}, {"start": 7.96, "end": 12.88, "text": " object detection with transformers from Facebook AI research and they have a"}, {"start": 12.88, "end": 16.6, "text": " GitHub repo and they pretty much giv... |
Yannic Kilcher | https://www.youtube.com/watch?v=SY5PvZrJhLE | GPT-3: Language Models are Few-Shot Learners (Paper Explained) | #gpt3 #openai #gpt-3
How far can you go with ONLY language modeling? Can a large enough language model perform NLP task out of the box? OpenAI take on these and other questions by training a transformer that is an order of magnitude larger than anything that has ever been built before and the results are astounding.
... | Hello there. Today we're looking at language models, our few shop learners by Tom B. Brown, Benjamin Mann, Nick Rider, and Melanie Sibaya, and a whole slew of authors from OpenAI. This paper also called GPT-3 just came out recently. GPT-3 is a model that is a language model, and it comes out of a succession of languag... | [{"start": 0.0, "end": 14.0, "text": " Hello there. Today we're looking at language models, our few shop learners by Tom B. Brown, Benjamin Mann, Nick Rider, and Melanie Sibaya, and a whole slew of authors from OpenAI."}, {"start": 14.0, "end": 34.0, "text": " This paper also called GPT-3 just came out recently. GPT-3 ... |
Yannic Kilcher | https://www.youtube.com/watch?v=T35ba_VXkMY | DETR: End-to-End Object Detection with Transformers (Paper Explained) | Object detection in images is a notoriously hard task! Objects can be of a wide variety of classes, can be numerous or absent, they can occlude each other or be out of frame. All of this makes it even more surprising that the architecture in this paper is so simple. Thanks to a clever loss function, a single Transforme... | Hi there. Today we're going to look at end-to-end object detection with transformers by Nicolas Carillon from Chiscomasa and others at Facebook AI research. So on a high level this paper does object detection in images using first a CNN and then a transformer to detect objects and it does so via a bipartite matching t... | [{"start": 0.0, "end": 4.44, "text": " Hi there. Today we're going to look at end-to-end object detection with"}, {"start": 4.44, "end": 9.72, "text": " transformers by Nicolas Carillon from Chiscomasa and others at Facebook AI"}, {"start": 9.72, "end": 16.8, "text": " research. So on a high level this paper does objec... |
Yannic Kilcher | https://www.youtube.com/watch?v=a-VQfQqIMrE | mixup: Beyond Empirical Risk Minimization (Paper Explained) | Neural Networks often draw hard boundaries in high-dimensional space, which makes them very brittle. Mixup is a technique that linearly interpolates between data and labels at training time and achieves much smoother and more regular class boundaries.
OUTLINE:
0:00 - Intro
0:30 - The problem with ERM
2:50 - Mixup
6:40... | Hi there, today we'll look at mix up beyond empirical risk minimization by Hongyi Changmushstaffa Sis, Yan, and Do Fan and David Lopez pass. So this paper is actually pretty simple, but it introduces a technique that apparently helps with training classifiers and I have it seen it used in practice. So there must be at... | [{"start": 0.0, "end": 7.54, "text": " Hi there, today we'll look at mix up beyond empirical risk minimization by Hongyi Changmushstaffa"}, {"start": 7.54, "end": 12.96, "text": " Sis, Yan, and Do Fan and David Lopez pass."}, {"start": 12.96, "end": 19.76, "text": " So this paper is actually pretty simple, but it intro... |
Yannic Kilcher | https://www.youtube.com/watch?v=l5he9JNJqHA | A critical analysis of self-supervision, or what we can learn from a single image (Paper Explained) | Does self-supervision really need a lot of data? How low can you go? This paper shows that a single image is enough to learn the lower layers of a deep neural network. Interestingly, more data does not appear to help as long as enough data augmentation is applied.
OUTLINE:
0:00 - Overview
1:40 - What is self-supervisi... | All right, today we'll look at a critical analysis of self-supervision or what we can learn from a single image by Yuki M. Asano, Christian Ruprecht and Andrea Vidaldi. This paper I really was excited when I saw this paper because the outset is so cool and the experiments have a very promising. So we'll take a look. B... | [{"start": 0.0, "end": 7.12, "text": " All right, today we'll look at a critical analysis of self-supervision or what we can learn"}, {"start": 7.12, "end": 15.4, "text": " from a single image by Yuki M. Asano, Christian Ruprecht and Andrea Vidaldi."}, {"start": 15.4, "end": 24.68, "text": " This paper I really was exc... |
Yannic Kilcher | https://www.youtube.com/watch?v=YrO1v7-KcXs | Deep image reconstruction from human brain activity (Paper Explained) | Can you peek into people's brains? Reading human thoughts is a long-standing dream of the AI field. This paper reads fMRI signals from a person and then reconstructs what that person's eyes currently see. This is achieved by translating the fMRI signal to features of a Deep Neural Network and then iteratively optimizin... | Hi there. Today we're looking at deep imagery construction from human brain activity by guawa Shen, Tomoyasu, Horikawa, Kai Majima and Yukiyazu Kamitani. This is like reading thoughts. So I was excited when I saw this paper, I saw this on Reddit and it is a bit older, it is from the beginning of last year. So I'm sure... | [{"start": 0.0, "end": 7.0, "text": " Hi there. Today we're looking at deep imagery construction from human brain activity by guawa"}, {"start": 7.0, "end": 20.0, "text": " Shen, Tomoyasu, Horikawa, Kai Majima and Yukiyazu Kamitani. This is like reading thoughts."}, {"start": 20.0, "end": 29.0, "text": " So I was excit... |
Yannic Kilcher | https://www.youtube.com/watch?v=UjJU13GdL94 | Regularizing Trajectory Optimization with Denoising Autoencoders (Paper Explained) | Can you plan with a learned model of the world? Yes, but there's a catch: The better your planning algorithm is, the more the errors of your world model will hurt you! This paper solves this problem by regularizing the planning algorithm to stay in high probability regions, given its experience.
https://arxiv.org/abs/... | Hi there. Today we're looking at regularizing trajectory optimization with denoising auto encoders by Renew Bonay, Normand de Paolo and others of various places but a lot of the people are from Curious AI and we actually had a discussion with Hari who is the CEO of Curious AI and this was on our machine learning stree... | [{"start": 0.0, "end": 4.98, "text": " Hi there. Today we're looking at regularizing trajectory optimization with"}, {"start": 4.98, "end": 11.48, "text": " denoising auto encoders by Renew Bonay, Normand de Paolo and others of various"}, {"start": 11.48, "end": 17.16, "text": " places but a lot of the people are from ... |
Yannic Kilcher | https://www.youtube.com/watch?v=wcHQ3IutSJg | [News] The NeurIPS Broader Impact Statement | For the first time, all authors submitting to the NeurIPS conference are forced to write a statement about the broader impact of their research on society. The messaging around this and how exactly this can influence the paper acceptance process is highly confusing.
OUTLINE:
0:00 - Intro
0:30 - VentureBeat Article
1:3... | As many of you might be familiar with, the NURB's 2020 conference now requires authors to include a section in their submissions discussing the broader impact of their work, including possible societal consequences both positive and negative. That was announced in the getting started with NURB's 2020 announcement on M... | [{"start": 0.0, "end": 18.0, "text": " As many of you might be familiar with, the NURB's 2020 conference now requires authors to include a section in their submissions discussing the broader impact of their work, including possible societal consequences both positive and negative."}, {"start": 18.0, "end": 25.0, "text"... |
Yannic Kilcher | https://www.youtube.com/watch?v=IIebBjbBevs | When BERT Plays the Lottery, All Tickets Are Winning (Paper Explained) | BERT is a giant model. Turns out you can prune away many of its components and it still works. This paper analyzes BERT pruning in light of the Lottery Ticket Hypothesis and finds that even the "bad" lottery tickets can be fine-tuned to good accuracy.
OUTLINE:
0:00 - Overview
1:20 - BERT
3:20 - Lottery Ticket Hypothes... | Hi there. Today we're looking at when Bert plays the lottery. All tickets are winning by Sai, Prasanna, Anna Rogers and Anna Rumsisky. So a high-level overview of this paper is the following. The paper basically looks at Bert in terms of the lottery ticket hypothesis and it says that if you find tune Bert on different... | [{"start": 0.0, "end": 5.38, "text": " Hi there. Today we're looking at when Bert plays the lottery. All tickets are"}, {"start": 5.38, "end": 11.58, "text": " winning by Sai, Prasanna, Anna Rogers and Anna Rumsisky. So a high-level"}, {"start": 11.58, "end": 16.26, "text": " overview of this paper is the following. Th... |
Yannic Kilcher | https://www.youtube.com/watch?v=utuz7wBGjKM | [News] OpenAI Model Generates Python Code | This code completion engine can write an entire function from just the name! OpenAI demonstrates what happens when you learn a language model on thousands of GitHub Python repositories.
Source Clip: https://youtu.be/fZSFNUT6iY8
Full Video: https://www.pscp.tv/Microsoft/1OyKAYWPRrWKb
Kite: https://kite.com/
TabNine: ht... | Hi there. So I saw this and probably many of you have seen this. OpenAI was demonstrating at MSBuild, basically a GPT2 language model but trained not on language but on code, on Python code, open source code from GitHub. And so the idea is that the model learns to produce code and we'll just have a short look at the c... | [{"start": 0.0, "end": 6.12, "text": " Hi there. So I saw this and probably many of you have seen this. OpenAI was"}, {"start": 6.12, "end": 12.52, "text": " demonstrating at MSBuild, basically a GPT2 language model but trained not on"}, {"start": 12.52, "end": 17.52, "text": " language but on code, on Python code, ope... |
Yannic Kilcher | https://www.youtube.com/watch?v=Nfry2b4RFI4 | Investigating Human Priors for Playing Video Games (Paper & Demo) | Why are humans so good at video games? Maybe it's because a lot of games are designed with humans in mind. What happens if we change that? This paper removes the influence of human priors from a game and ends up with a pretty fun experience.
Paper: https://arxiv.org/abs/1802.10217
Website: https://rach0012.github.io/h... | Hey there, what's going on today? We're looking at investigating human priors for playing video games by Ratchet Dubi, Pukit, Agrawal, Deepak Patak, Tom Griffiths, and Alexei, IEfras. So there is a paper to go with this, but I actually don't want to get into the paper too much, in order to not reveal too much of what'... | [{"start": 0.0, "end": 5.16, "text": " Hey there, what's going on today? We're looking at investigating human priors for playing video games by Ratchet Dubi,"}, {"start": 5.16, "end": 10.72, "text": " Pukit, Agrawal, Deepak Patak, Tom Griffiths, and Alexei, IEfras."}, {"start": 10.72, "end": 15.24, "text": " So there i... |
Yannic Kilcher | https://www.youtube.com/watch?v=u5BkO8XMS2I | iMAML: Meta-Learning with Implicit Gradients (Paper Explained) | Gradient-based Meta-Learning requires full backpropagation through the inner optimization procedure, which is a computational nightmare. This paper is able to circumvent this and implicitly compute meta-gradients by the clever introduction of a quadratic regularizer.
OUTLINE:
0:00 - Intro
0:15 - What is Meta-Learning?... | Hi there. Today we're looking at meta learning with implicit gradients by R-Wind Rajeshwaran, Chelsea Finn, Shomkakad and Sergei Levine. So this paper deals with the task of meta learning. Now if you don't know what meta learning is, let me quickly introduce the term. So in meta learning you assume you have some sort ... | [{"start": 0.0, "end": 5.04, "text": " Hi there. Today we're looking at meta learning with implicit gradients by"}, {"start": 5.04, "end": 12.84, "text": " R-Wind Rajeshwaran, Chelsea Finn, Shomkakad and Sergei Levine. So this paper"}, {"start": 12.84, "end": 17.12, "text": " deals with the task of meta learning. Now i... |
Yannic Kilcher | https://www.youtube.com/watch?v=G3pOvrKkFuk | [Code] PyTorch sentiment classifier from scratch with Huggingface NLP Library (Full Tutorial) | Huggingface released its newest library called NLP, which gives you easy access to almost any NLP dataset and metric in one convenient interface. We will combine this with a BERT model from Huggingface's Transformers library to build a sentiment classifier for IMDB.
OUTLINE:
0:00 - Intro
1:30 - Boilerplate
3:20 - PyTo... | How did it really do? So, HuggingFace just released this NLP library right here. And this is pretty cool because it allows you access to about 100 NLP data sets and 10 evaluation metrics pre-packaged. So knowing HuggingFace, this is going to be a breeze to work with. So what I thought we would do is we would try to us... | [{"start": 0.0, "end": 2.56, "text": " How did it really do?"}, {"start": 2.56, "end": 7.48, "text": " So, HuggingFace just released this NLP library right here."}, {"start": 7.48, "end": 14.92, "text": " And this is pretty cool because it allows you access to about 100 NLP data sets and 10"}, {"start": 14.92, "end": 1... |
Yannic Kilcher | https://www.youtube.com/watch?v=IiBFqnNu7A8 | Planning to Explore via Self-Supervised World Models (Paper Explained) | What can an agent do without any reward? Explore the world! While many formulations of intrinsic rewards exist (Curiosity, Novelty, etc.), they all look back in time to learn. Plan2Explore is the first model that uses planning in a learned imaginary latent world model to seek out states where it is uncertain about what... | Hi there. Today we're looking at planning to explore via self-supervised world models by Ramanan Sekar, Ole Ribkin, Kostas Danieli, Dis Pieter Abil, Dani Gerhoffner, and Depak Patak. So this is a paper that concerns reinforcement learning and specifically, sort of self-supervised reinforcement learning. So what do the... | [{"start": 0.0, "end": 6.8, "text": " Hi there. Today we're looking at planning to explore via self-supervised world models"}, {"start": 6.8, "end": 14.8, "text": " by Ramanan Sekar, Ole Ribkin, Kostas Danieli, Dis Pieter Abil, Dani Gerhoffner, and"}, {"start": 14.8, "end": 23.76, "text": " Depak Patak. So this is a pa... |
Yannic Kilcher | https://www.youtube.com/watch?v=XvDzZwoQFcU | [News] Facebook's Real-Time TTS system runs on CPUs only! | Facebook AI's new Text-To-Speech system is able to create 1 second of speech in as little as 500ms, making it real-time. What's even more impressive is the fact that this does not require a rack of GPUs, but runs on merely 4 CPUs.
OUTLINE:
0:00 - Intro
1:00 - Problem Formulation
3:20 - System Explanation
15:00 - Speed... | Hi there, check this out. Modern text-to-speech systems have come a long way in using neural networks to mimic the nuances of human voice. To generate human-like audio, one second of speech can require a TTS system to output as many as 24,000 samples, sometimes even more. The size and complexity of state-of-the-art mo... | [{"start": 0.0, "end": 4.0, "text": " Hi there, check this out."}, {"start": 4.0, "end": 8.44, "text": " Modern text-to-speech systems have come a long way in using neural networks to mimic the"}, {"start": 8.44, "end": 10.56, "text": " nuances of human voice."}, {"start": 10.56, "end": 15.6, "text": " To generate huma... |
Yannic Kilcher | https://www.youtube.com/watch?v=p-zOeQCoG9c | Weight Standardization (Paper Explained) | It's common for neural networks to include data normalization such as BatchNorm or GroupNorm. This paper extends the normalization to also include the weights of the network. This surprisingly simple change leads to a boost in performance and - combined with GroupNorm - new state-of-the-art results.
https://arxiv.org/... | Hi there! Today we're looking at Weight Standardization by Siwon Jao, who you Wang Shen Qi Yu Wei Shen, Alan Yule of John Hopkins University. So Weight Standardization is a normalization technique for training neural networks and it goes basically in conjunction with another technique called Group Normalization. So if... | [{"start": 0.0, "end": 7.34, "text": " Hi there! Today we're looking at Weight Standardization by Siwon Jao, who you"}, {"start": 7.34, "end": 15.44, "text": " Wang Shen Qi Yu Wei Shen, Alan Yule of John Hopkins University. So Weight"}, {"start": 15.44, "end": 22.04, "text": " Standardization is a normalization techniq... |
Yannic Kilcher | https://www.youtube.com/watch?v=zt_R85Ife_U | [Trash] Automated Inference on Criminality using Face Images | This paper sets out to build a classifier to distinguish criminals from non-criminals using nothing but a face picture. I explore why the research is trash and what lessons we can learn from it.
https://arxiv.org/abs/1611.04135
Links:
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilc... | Hi there. Take a look at these faces. Try to decide which of these faces are criminals and which ones are law abiding citizens. I'll give you a second. Okay, got it? So if you decided that these four here are the criminals, you would be correct. And that makes these three the law abiding citizens. As for this one, may... | [{"start": 0.0, "end": 10.0, "text": " Hi there. Take a look at these faces. Try to decide which of these faces are criminals and which ones are law abiding citizens."}, {"start": 10.0, "end": 12.0, "text": " I'll give you a second."}, {"start": 13.0, "end": 20.0, "text": " Okay, got it? So if you decided that these fo... |
Yannic Kilcher | https://www.youtube.com/watch?v=bFn2xcGi1TQ | Faster Neural Network Training with Data Echoing (Paper Explained) | CPUs are often bottlenecks in Machine Learning pipelines. Data fetching, loading, preprocessing and augmentation can be slow to a point where the GPUs are mostly idle. Data Echoing is a technique to re-use data that is already in the pipeline to reclaim this idle time and keep the GPUs busy at all times.
https://arxiv... | Hi there, today we're looking at faster neural network training with data echoing by Dami Choi, Alexander Paso, Christopher J, Shallyu and George E. Dahl. So on a high level this paper basically says you should repeat data that's already in memory in order to speed up the entire process of neural network training. And... | [{"start": 0.0, "end": 6.32, "text": " Hi there, today we're looking at faster neural network training with data echoing by"}, {"start": 6.32, "end": 12.92, "text": " Dami Choi, Alexander Paso, Christopher J, Shallyu and George E. Dahl. So on a"}, {"start": 12.92, "end": 18.06, "text": " high level this paper basically... |
Yannic Kilcher | https://www.youtube.com/watch?v=l_3zj6HeWUE | Group Normalization (Paper Explained) | The dirty little secret of Batch Normalization is its intrinsic dependence on the training batch size. Group Normalization attempts to achieve the benefits of normalization without batch statistics and, most importantly, without sacrificing performance compared to Batch Normalization.
https://arxiv.org/abs/1803.08494
... | Hi there. Today we'll look at group normalization by Yushin Wu and Kaimin He of Facebook AI research. So this paper is basically an engineering paper about a new normalization technique called group normalization. So what's the issue here? The issue is that pretty much throughout neural network learning, we're using t... | [{"start": 0.0, "end": 10.120000000000001, "text": " Hi there. Today we'll look at group normalization by Yushin Wu and Kaimin He of Facebook AI research."}, {"start": 10.120000000000001, "end": 16.4, "text": " So this paper is basically an engineering paper about a new normalization technique called"}, {"start": 16.4,... |
Yannic Kilcher | https://www.youtube.com/watch?v=Cs_j-oNwGgg | Concept Learning with Energy-Based Models (Paper Explained) | This is a hard paper! Energy-functions are typically a mere afterthought in current machine learning. A core function of the Energy - its smoothness - is usually not exploited at inference time. This paper takes a stab at it. Inferring concepts, world states, and attention masks via gradient descent on a learned energy... | Hi there. What you're seeing here is an energy-based model that learns the concept of a shape from a demonstration on the left. So on the left you can see a demonstration of data point sampled from a shape in these cases circles or squares. And then the corresponding energy function that the model infers from that. An... | [{"start": 0.0, "end": 8.0, "text": " Hi there. What you're seeing here is an energy-based model that learns the concept of a shape"}, {"start": 8.0, "end": 14.0, "text": " from a demonstration on the left. So on the left you can see a demonstration of data point"}, {"start": 14.0, "end": 21.0, "text": " sampled from a... |
Yannic Kilcher | https://www.youtube.com/watch?v=iZXsWlSdMGY | [News] Google’s medical AI was super accurate in a lab. Real life was a different story. | A closer look at a story of how the deployment of AI brings its own challenges and what can go wrong.
https://www.technologyreview.com/2020/04/27/1000658/google-medical-ai-accurate-lab-real-life-clinic-covid-diabetes-retina-disease/
Links:
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/... | Hi there. Today we're looking at this new story from MIT Technology Review. Google's Medical AI was super accurate in a lab. Real life was a different story. So, this story here is that Google had this AI to detect diabetic retinopathy. So, if you're a diabetic and your glucose isn't or your insulin isn't properly han... | [{"start": 0.0, "end": 5.5200000000000005, "text": " Hi there. Today we're looking at this new story from MIT Technology Review."}, {"start": 5.5200000000000005, "end": 8.52, "text": " Google's Medical AI was super accurate in a lab."}, {"start": 8.52, "end": 10.92, "text": " Real life was a different story."}, {"start... |
Yannic Kilcher | https://www.youtube.com/watch?v=k1GOF2jmX7c | Big Transfer (BiT): General Visual Representation Learning (Paper Explained) | One CNN to rule them all! BiT is a pre-trained ResNet that can be used as a starting point for any visual task. This paper explains what it takes to pre-train such a large model and details how fine-tuning on downstream tasks is done best.
Paper: https://arxiv.org/abs/1912.11370
Code & Models: TBA
Abstract:
Transfer ... | Hi there. Today we're talking about big transfer general visual representation learning by Alexander Kolesnikov, Lukas Bair, Jawah Chai and others of Google Brain. So this paper is basically an application slash engineering paper for the community and it is about the task of transfer learning for visual tasks. So what... | [{"start": 0.0, "end": 5.76, "text": " Hi there. Today we're talking about big transfer general visual representation"}, {"start": 5.76, "end": 12.64, "text": " learning by Alexander Kolesnikov, Lukas Bair, Jawah Chai and others of Google"}, {"start": 12.64, "end": 20.240000000000002, "text": " Brain. So this paper is ... |
Yannic Kilcher | https://www.youtube.com/watch?v=tjbEVY5XIk0 | Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning (Paper Explained) | When AI makes a plan it usually does so step by step, forward in time. But often it is beneficial to define intermediate goals to divide a large problem into easier sub-problems. This paper proposes a generalization of MCTS that searches not for the best next actions to take, but for the best way to sub-divide the prob... | Hi there. What you're seeing here is a divide and conquer Monte Carlo tree search in action. This is a planning algorithm that plans in a kind of an unconventional fashion. So we're going to explore this today in this paper, dividing conquer Monte Carlo tree search for goal directed planning by Jamba Tista, Parascondo... | [{"start": 0.0, "end": 7.640000000000001, "text": " Hi there. What you're seeing here is a divide and conquer Monte Carlo tree search in action."}, {"start": 7.640000000000001, "end": 15.52, "text": " This is a planning algorithm that plans in a kind of an unconventional fashion. So we're"}, {"start": 15.52, "end": 20.... |
Yannic Kilcher | https://www.youtube.com/watch?v=eCH0M4wzKJs | WHO ARE YOU? 10k Subscribers Special (w/ Channel Analytics) | An in-depth look at this channel's analytics.
Links:
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilcher
BitChute: https://www.bitchute.com/channel/yannic-kilcher
Minds: https://www.minds.com/ykilcher | Hi there, we have just crossed 10,000 subscribers on this channel and that is an absolutely mind blowing number. To everyone who's subscribed, thank you and today for a bit of a special occasion I thought we would look at you. Yes, you handsome, one of the 10,000 subscribers of this channel and we're going to dive int... | [{"start": 0.0, "end": 6.2, "text": " Hi there, we have just crossed 10,000 subscribers on this channel and that is an"}, {"start": 6.2, "end": 11.32, "text": " absolutely mind blowing number. To everyone who's subscribed, thank you and"}, {"start": 11.32, "end": 16.6, "text": " today for a bit of a special occasion I ... |
Yannic Kilcher | https://www.youtube.com/watch?v=to7vCdkLi4s | Reinforcement Learning with Augmented Data (Paper Explained) | This ONE SIMPLE TRICK can take a vanilla RL algorithm to achieve state-of-the-art. What is it? Simply augment your training data before feeding it to the learner! This can be dropped into any RL pipeline and promises big improvements across the board.
Paper: https://arxiv.org/abs/2004.14990
Code: https://www.github.co... | Hi there. Today we're going to take a short look at reinforcement learning with augmented data. This paper is by Michael Laskin, Kimine Lee, and others from UC Berkeley and NYU. So the reason why this is a short look is because I believe the statements made in the paper are quite short and small, but they are quite gr... | [{"start": 0.0, "end": 5.74, "text": " Hi there. Today we're going to take a short look at reinforcement learning with augmented"}, {"start": 5.74, "end": 12.68, "text": " data. This paper is by Michael Laskin, Kimine Lee, and others from UC Berkeley and NYU."}, {"start": 12.68, "end": 17.54, "text": " So the reason wh... |
Yannic Kilcher | https://www.youtube.com/watch?v=cIUtRNhY6Rw | TAPAS: Weakly Supervised Table Parsing via Pre-training (Paper Explained) | Answering complex questions about tabular information is hard. No two tables are alike and sometimes the answer you're looking for is not even in the table and needs to be computed from a subset of the cells. Surprisingly, this model can figure it all out by itself through some clever input encoding and loss engineerin... | Hi there, have a look at this table on the left. So in this table, in each row, you can see following things. The name of a wrestler, the number of times that wrestler has been a champion, and the combined number of days where that wrestler has been a champion, or like the sum of the length of all their championships.... | [{"start": 0.0, "end": 4.14, "text": " Hi there, have a look at this table on the left."}, {"start": 4.14, "end": 8.78, "text": " So in this table, in each row, you can see following things."}, {"start": 8.78, "end": 16.1, "text": " The name of a wrestler, the number of times that wrestler has been a champion, and the"... |
Yannic Kilcher | https://www.youtube.com/watch?v=PDRtyrVskMU | Chip Placement with Deep Reinforcement Learning (Paper Explained) | The AI Singularity is here! Computers designing new computers! It takes human experts multiple weeks to design new computer chips. What looks like a large game of Tetris is actually a very complex optimization problem. This paper uses Deep Reinforcement Learning to solve this optimization both faster and better than hu... | Hi there. Today we're looking at Chip Placement with Deep Reinforcement Learning by Azalia Mirazini on a Goldie and a long list of authors that I have no stamina to read down. I'm sorry. So this work is a cool application of reinforcement learning to the real world. And we're gonna go through it and the cool thing abo... | [{"start": 0.0, "end": 8.0, "text": " Hi there. Today we're looking at Chip Placement with Deep Reinforcement Learning by Azalia Mirazini"}, {"start": 8.0, "end": 15.0, "text": " on a Goldie and a long list of authors that I have no stamina to read down. I'm sorry."}, {"start": 15.0, "end": 23.0, "text": " So this work... |
Yannic Kilcher | https://www.youtube.com/watch?v=wTIPGoHLw_8 | I talk to the new Facebook Blender Chatbot | This is what a 9 Billion parameter transformer can do. I take a look at FAIR's new paper "Recipes for building an open-domain chatbot" and try out their chatbot live!
Jump to 3:00 to see the chatbot in action.
Paper: https://arxiv.org/abs/2004.13637
Blog: https://ai.facebook.com/blog/state-of-the-art-open-source-chat... | Yes, I am a vegan. I don't eat any animal products. Hi there. Today we're going to talk to a transformer and specifically to the new chatbot blender that Facebook has just released. Everything is open source so we can try it out live. Now along with the code, they've released this paper here called Recipes for Buildin... | [{"start": 0.0, "end": 4.42, "text": " Yes, I am a vegan. I don't eat any animal products."}, {"start": 4.42, "end": 10.76, "text": " Hi there. Today we're going to talk to a transformer and specifically to the new chatbot"}, {"start": 10.76, "end": 17.32, "text": " blender that Facebook has just released. Everything i... |
Yannic Kilcher | https://www.youtube.com/watch?v=1aO-uHXbzmQ | Jukebox: A Generative Model for Music (Paper Explained) | This generative model for music can make entire songs with remarkable quality and consistency. It can be conditioned on genre, artist, and even lyrics.
Blog: https://openai.com/blog/jukebox/
Paper: https://cdn.openai.com/papers/jukebox.pdf
Code: https://github.com/openai/jukebox/
Abstract:
We introduce Jukebox, a mod... | Alright, so what you're hearing is the open AI jukes box. This paper came out and it is a surprisingly good quality generative model for music, including lyrics, so including singing, which I believe is pretty novel, and the fact that it works so well and has musical consistency throughout entire songs is something th... | [{"start": 0.0, "end": 25.36, "text": " Alright, so what you're hearing is the open AI jukes box."}, {"start": 25.36, "end": 50.36, "text": " This paper came out and it is a surprisingly good quality generative model for music, including lyrics, so including singing, which I believe is pretty novel, and the fact that i... |
Yannic Kilcher | https://www.youtube.com/watch?v=RrBapqCPnmE | [ML Coding Tips] Separate Computation & Plotting using locals | Here's a lazy way to separate computation and subsequent analysis in a notebook without the overhead of manually saving local variables.
WARNING: Don't do this in a serious project.
Links:
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilcher
BitChute: https://www.bitchute.com/channel... | Hi there. So today I just wanted to bring you a quick coding tip that I often encounter in my daily machine learning researcher life that might not be super common in let's say traditional software engineering or elsewhere. So often I have a bunch of let's say I have a bunch of models right and I use these ipython not... | [{"start": 0.0, "end": 16.0, "text": " Hi there. So today I just wanted to bring you a quick coding tip that I often encounter in my daily machine learning researcher life that might not be super common in let's say traditional software engineering or elsewhere."}, {"start": 16.0, "end": 26.0, "text": " So often I have... |
Yannic Kilcher | https://www.youtube.com/watch?v=F5aaXrIMWyU | The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies (Paper Explained) | Hail the AI Tax Collector! This very visual framework has RL Agents maximize their coins in a tiny world through collecting, building and trading. But at the same time, the government is also an AI trying to maximize social welfare via taxes. What emerges is very interesting.
Paper: https://arxiv.org/abs/2004.13332
Bl... | Alright, today we're going to find out why AI is much better at governing people, why poor people really should pay more taxes and how Donald Trump is just a normal human. Alright, we'll dive into it. We're looking at the AI economist by Salesforce Research. Now Salesforce Research has kind of created a simulated worl... | [{"start": 0.0, "end": 12.0, "text": " Alright, today we're going to find out why AI is much better at governing people, why poor people really should pay more taxes and how Donald Trump is just a normal human."}, {"start": 12.0, "end": 19.0, "text": " Alright, we'll dive into it. We're looking at the AI economist by S... |
Yannic Kilcher | https://www.youtube.com/watch?v=jhCInVFE2sc | Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask (Paper Explained) | This paper dives into the intrinsics of the Lottery Ticket Hypothesis and attempts to shine some light on what's important and what isn't.
https://arxiv.org/abs/1905.01067
Abstract:
The recent "Lottery Ticket Hypothesis" paper by Frankle & Carbin showed that a simple approach to creating sparse networks (keeping the ... | Hi there! Today we're looking at deconstructing lottery tickets, zeros, signs, and the supermask by Hadi Jo, Janis Lahn, Rosanne Liu, and Jason Yosinski of Uber AI. So this is a follower paper to the original paper that was called the lottery ticket hypothesis. I have done a video on that paper, so if you don't know w... | [{"start": 0.0, "end": 13.0, "text": " Hi there! Today we're looking at deconstructing lottery tickets, zeros, signs, and the supermask by Hadi Jo, Janis Lahn, Rosanne Liu, and Jason Yosinski of Uber AI."}, {"start": 13.0, "end": 20.0, "text": " So this is a follower paper to the original paper that was called the lott... |
Yannic Kilcher | https://www.youtube.com/watch?v=h9w3KffPPmQ | [Rant] Online Conferences | Are virtual conferences good or bad? What's missing? How do we go forward?
Pictures from here:
https://twitter.com/srush_nlp/status/1253786329575538691
Links:
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilcher
BitChute: https://www.bitchute.com/channel/yannic-kilcher
Minds: https:... | Hey machine learners, young Ikea. Okay, that is stolen. Today I want to give some quick thoughts about online conferences. As you might know, I clear this year is fully online because of the global situation. Big props to the organizers of the conference for putting something together in this short amount of time. Ike... | [{"start": 0.0, "end": 3.68, "text": " Hey machine learners, young Ikea."}, {"start": 3.68, "end": 5.4, "text": " Okay, that is stolen."}, {"start": 5.4, "end": 9.68, "text": " Today I want to give some quick thoughts about online conferences."}, {"start": 9.68, "end": 15.92, "text": " As you might know, I clear this y... |
Yannic Kilcher | https://www.youtube.com/watch?v=fvctpYph8Pc | Do ImageNet Classifiers Generalize to ImageNet? (Paper Explained) | Has the world overfitted to ImageNet? What if we collect another dataset in exactly the same fashion? This paper gives a surprising answer!
Paper: https://arxiv.org/abs/1902.10811
Data: https://github.com/modestyachts/ImageNetV2
Abstract:
We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks ... | Hi there, today we're looking at to do ImageNet classifiers generalized to ImageNet by Benjamin Rekt, Rebecca Rolofs, Ludwig Schmidt and Vaishal Shankar. So the premise of this paper is pretty simple. We've been training models on ImageNet now for a while, almost 10 years to be exact. ImageNet is this data set with a ... | [{"start": 0.0, "end": 5.8, "text": " Hi there, today we're looking at to do ImageNet classifiers generalized to ImageNet by Benjamin"}, {"start": 5.8, "end": 10.68, "text": " Rekt, Rebecca Rolofs, Ludwig Schmidt and Vaishal Shankar."}, {"start": 10.68, "end": 13.56, "text": " So the premise of this paper is pretty sim... |
Yannic Kilcher | https://www.youtube.com/watch?v=hDQNCWR3HLQ | [Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton | Schmidhuber writes up a critique of Hinton receiving the Honda Price... AND HINTON REPLIES!
Schmidhuber's Blog Entry: http://people.idsia.ch/~juergen/critique-honda-prize-hinton.html
Hinton's Reply: https://www.reddit.com/r/MachineLearning/comments/g5ali0/d_schmidhuber_critique_of_honda_prize_for_dr/
Thumbnail Images... | On April 21st, the Irgunshmiduver tweeted out, stop crediting the wrong people for inventions made by others. At least in science, the facts will always win at the end, as long as the facts have not yet won. It is not yet the end. No fancy award can ever change that. Hashtag self-correcting science, hashtag plagiarism... | [{"start": 0.0, "end": 8.0, "text": " On April 21st, the Irgunshmiduver tweeted out, stop crediting the wrong people for inventions made by others."}, {"start": 8.0, "end": 14.0, "text": " At least in science, the facts will always win at the end, as long as the facts have not yet won."}, {"start": 14.0, "end": 16.0, "... |
Yannic Kilcher | https://www.youtube.com/watch?v=gJR28onlqzs | How much memory does Longformer use? | A calculation of the memory requirements of the Longformer.
Original video: https://youtu.be/_8KNb5iqblE
Paper: https://arxiv.org/abs/2004.05150
Links:
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilcher
BitChute: https://www.bitchute.com/channel/yannic-kilcher
Minds: https://www.mi... | So I wanted to come back to this paper here about the long former. I have done a video on this. If you haven't seen it, then this video is probably not going to make much sense to you, but in the video I go over what the long former is, what it does, how it compares, and so on. And the gist of the long former is that ... | [{"start": 0.0, "end": 7.44, "text": " So I wanted to come back to this paper here about the long former. I have done a"}, {"start": 7.44, "end": 11.040000000000001, "text": " video on this. If you haven't seen it, then this video is probably not going to"}, {"start": 11.040000000000001, "end": 16.28, "text": " make mu... |
Yannic Kilcher | https://www.youtube.com/watch?v=MpdbFLXOOIw | Supervised Contrastive Learning | The cross-entropy loss has been the default in deep learning for the last few years for supervised learning. This paper proposes a new loss, the supervised contrastive loss, and uses it to pre-train the network in a supervised fashion. The resulting model, when fine-tuned to ImageNet, achieves new state-of-the-art.
ht... | Hi there, today we're looking at supervised contrastive learning by people from Google Research and MIT. Now, this paper proposes a new loss for supervised learning. And you might recognize that this is a big claim. So, forever now, we've basically used this cross entropy loss in order to do supervised training of neu... | [{"start": 0.0, "end": 6.0, "text": " Hi there, today we're looking at supervised contrastive learning by people from Google"}, {"start": 6.0, "end": 8.0, "text": " Research and MIT."}, {"start": 8.0, "end": 14.0, "text": " Now, this paper proposes a new loss for supervised learning."}, {"start": 14.0, "end": 19.0, "te... |
Yannic Kilcher | https://www.youtube.com/watch?v=pZyxlf6l0N8 | Thinking While Moving: Deep Reinforcement Learning with Concurrent Control | Classic RL "stops" the world whenever the Agent computes a new action. This paper considers a more realistic scenario where the agent is thinking about the next action to take while still performing the last action. This results in a fascinating way of reformulating Q-learning in continuous time, then introducing concu... | Hi there. So if you look at these two robots, the left one labeled blocking the right one labeled concurrent. The blocking robot, as you can see, always has these little pauses in its movement, where it does nothing. And then it kind of continues with its motion, while the one on the right is one continuous motion tha... | [{"start": 0.0, "end": 7.0, "text": " Hi there. So if you look at these two robots, the left one labeled blocking the right one labeled concurrent."}, {"start": 7.0, "end": 15.0, "text": " The blocking robot, as you can see, always has these little pauses in its movement, where it does nothing."}, {"start": 15.0, "end"... |
Yannic Kilcher | https://www.youtube.com/watch?v=yPjuAo53uNI | [Rant] The Male Only History of Deep Learning | This casting of our field in terms of ideological narrow-sighted group-think is disgusting. Keep Science about ideas!
https://twitter.com/timnitGebru/status/1252752743942328321
Links:
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilcher
BitChute: https://www.bitchute.com/channel/yann... | Alright, so instead of reviewing a paper today, I thought I might review this thing. So this person on Twitter posted this link to an article called Brief History of Deep Learning from 1943 to 2019 of Machine Learning Knowledge.ai. So let's look at this. Actually let's look at the tweet first because this is, I just s... | [{"start": 0.0, "end": 6.36, "text": " Alright, so instead of reviewing a paper today, I thought I might review this thing."}, {"start": 6.36, "end": 13.32, "text": " So this person on Twitter posted this link to an article called Brief History of Deep"}, {"start": 13.32, "end": 21.2, "text": " Learning from 1943 to 20... |
Yannic Kilcher | https://www.youtube.com/watch?v=PZypP7PiKi0 | Gradient Surgery for Multi-Task Learning | Multi-Task Learning can be very challenging when gradients of different tasks are of severely different magnitudes or point into conflicting directions. PCGrad eliminates this problem by projecting conflicting gradients while still retaining optimality guarantees.
https://arxiv.org/abs/2001.06782
Abstract:
While deep... | Hi there. Today we're looking at gradient surgery for multi-task learning by Tian He Yu, Saurab Kumar, Abhishek Kupta, Sergei Levine, Carol Haosman and Chelsea Finn. So in this paper the the concern is a thing called multi-task learning. Now what is multi-task learning? So this has some very subtle distinctions from o... | [{"start": 0.0, "end": 5.6000000000000005, "text": " Hi there. Today we're looking at gradient surgery for multi-task learning by"}, {"start": 5.6000000000000005, "end": 13.280000000000001, "text": " Tian He Yu, Saurab Kumar, Abhishek Kupta, Sergei Levine, Carol Haosman and"}, {"start": 13.280000000000001, "end": 20.8,... |
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