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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,502.01852 | Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification | ['Kaiming He', 'Xiangyu Zhang', 'Shaoqing Ren', 'Jian Sun'] | ['cs.CV', 'cs.AI', 'cs.LG'] | Rectified activation units (rectifiers) are essential for state-of-the-art
neural networks. In this work, we study rectifier neural networks for image
classification from two aspects. First, we propose a Parametric Rectified
Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU
improves model fitti... | 2015-02-06T10:44:00Z | null | null | null | null | null | null | null | null | null | null |
1,502.03044 | Show, Attend and Tell: Neural Image Caption Generation with Visual
Attention | ['Kelvin Xu', 'Jimmy Ba', 'Ryan Kiros', 'Kyunghyun Cho', 'Aaron Courville', 'Ruslan Salakhutdinov', 'Richard Zemel', 'Yoshua Bengio'] | ['cs.LG', 'cs.CV'] | Inspired by recent work in machine translation and object detection, we
introduce an attention based model that automatically learns to describe the
content of images. We describe how we can train this model in a deterministic
manner using standard backpropagation techniques and stochastically by
maximizing a variation... | 2015-02-10T19:18:29Z | null | null | null | null | null | null | null | null | null | null |
1,502.05698 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | ['Jason Weston', 'Antoine Bordes', 'Sumit Chopra', 'Alexander M. Rush', 'Bart van Merriënboer', 'Armand Joulin', 'Tomas Mikolov'] | ['cs.AI', 'cs.CL', 'stat.ML'] | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question a... | 2015-02-19T20:46:10Z | null | null | null | null | null | null | null | null | null | null |
1,503.02531 | Distilling the Knowledge in a Neural Network | ['Geoffrey Hinton', 'Oriol Vinyals', 'Jeff Dean'] | ['stat.ML', 'cs.LG', 'cs.NE'] | A very simple way to improve the performance of almost any machine learning
algorithm is to train many different models on the same data and then to
average their predictions. Unfortunately, making predictions using a whole
ensemble of models is cumbersome and may be too computationally expensive to
allow deployment to... | 2015-03-09T15:44:49Z | NIPS 2014 Deep Learning Workshop | null | null | Distilling the Knowledge in a Neural Network | ['Geoffrey E. Hinton', 'O. Vinyals', 'J. Dean'] | 2,015 | arXiv.org | 19,824 | 9 | ['Mathematics', 'Computer Science'] |
1,503.03832 | FaceNet: A Unified Embedding for Face Recognition and Clustering | ['Florian Schroff', 'Dmitry Kalenichenko', 'James Philbin'] | ['cs.CV'] | Despite significant recent advances in the field of face recognition,
implementing face verification and recognition efficiently at scale presents
serious challenges to current approaches. In this paper we present a system,
called FaceNet, that directly learns a mapping from face images to a compact
Euclidean space whe... | 2015-03-12T18:10:53Z | Also published, in Proceedings of the IEEE Computer Society
Conference on Computer Vision and Pattern Recognition 2015 | null | 10.1109/CVPR.2015.7298682 | FaceNet: A unified embedding for face recognition and clustering | ['Florian Schroff', 'Dmitry Kalenichenko', 'James Philbin'] | 2,015 | Computer Vision and Pattern Recognition | 13,210 | 24 | ['Computer Science'] |
1,504.00325 | Microsoft COCO Captions: Data Collection and Evaluation Server | ['Xinlei Chen', 'Hao Fang', 'Tsung-Yi Lin', 'Ramakrishna Vedantam', 'Saurabh Gupta', 'Piotr Dollar', 'C. Lawrence Zitnick'] | ['cs.CV', 'cs.CL'] | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in e... | 2015-04-01T18:13:43Z | arXiv admin note: text overlap with arXiv:1411.4952 | null | null | null | null | null | null | null | null | null |
1,504.06375 | Holistically-Nested Edge Detection | ['Saining Xie', 'Zhuowen Tu'] | ['cs.CV'] | We develop a new edge detection algorithm that tackles two important issues
in this long-standing vision problem: (1) holistic image training and
prediction; and (2) multi-scale and multi-level feature learning. Our proposed
method, holistically-nested edge detection (HED), performs image-to-image
prediction by means o... | 2015-04-24T02:12:15Z | v2 Add appendix A for updated results (ODS=0.790) on BSDS-500 in a
new experiment setting. Fix typos and reorganize formulations. Add Table 2 to
discuss the role of deep supervision. Add links to publicly available
repository for code, models and data | null | null | Holistically-Nested Edge Detection | ['Saining Xie', 'Z. Tu'] | 2,015 | International Journal of Computer Vision | 3,503 | 59 | ['Computer Science'] |
1,504.08083 | Fast R-CNN | ['Ross Girshick'] | ['cs.CV'] | This paper proposes a Fast Region-based Convolutional Network method (Fast
R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently
classify object proposals using deep convolutional networks. Compared to
previous work, Fast R-CNN employs several innovations to improve training and
testing speed w... | 2015-04-30T05:13:08Z | To appear in ICCV 2015 | null | null | Fast R-CNN | ['Ross B. Girshick'] | 2,015 | null | 25,181 | 23 | ['Computer Science'] |
1,505.04597 | U-Net: Convolutional Networks for Biomedical Image Segmentation | ['Olaf Ronneberger', 'Philipp Fischer', 'Thomas Brox'] | ['cs.CV'] | There is large consent that successful training of deep networks requires
many thousand annotated training samples. In this paper, we present a network
and training strategy that relies on the strong use of data augmentation to use
the available annotated samples more efficiently. The architecture consists of
a contrac... | 2015-05-18T11:28:37Z | conditionally accepted at MICCAI 2015 | null | null | null | null | null | null | null | null | null |
1,505.0487 | Flickr30k Entities: Collecting Region-to-Phrase Correspondences for
Richer Image-to-Sentence Models | ['Bryan A. Plummer', 'Liwei Wang', 'Chris M. Cervantes', 'Juan C. Caicedo', 'Julia Hockenmaier', 'Svetlana Lazebnik'] | ['cs.CV', 'cs.CL'] | The Flickr30k dataset has become a standard benchmark for sentence-based
image description. This paper presents Flickr30k Entities, which augments the
158k captions from Flickr30k with 244k coreference chains, linking mentions of
the same entities across different captions for the same image, and associating
them with ... | 2015-05-19T04:46:03Z | null | null | null | null | null | null | null | null | null | null |
1,506.01497 | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal
Networks | ['Shaoqing Ren', 'Kaiming He', 'Ross Girshick', 'Jian Sun'] | ['cs.CV'] | State-of-the-art object detection networks depend on region proposal
algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN
have reduced the running time of these detection networks, exposing region
proposal computation as a bottleneck. In this work, we introduce a Region
Proposal Network (RPN)... | 2015-06-04T07:58:34Z | Extended tech report | null | null | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | ['Shaoqing Ren', 'Kaiming He', 'Ross B. Girshick', 'Jian Sun'] | 2,015 | IEEE Transactions on Pattern Analysis and Machine Intelligence | 62,776 | 47 | ['Computer Science', 'Medicine'] |
1,506.02025 | Spatial Transformer Networks | ['Max Jaderberg', 'Karen Simonyan', 'Andrew Zisserman', 'Koray Kavukcuoglu'] | ['cs.CV'] | Convolutional Neural Networks define an exceptionally powerful class of
models, but are still limited by the lack of ability to be spatially invariant
to the input data in a computationally and parameter efficient manner. In this
work we introduce a new learnable module, the Spatial Transformer, which
explicitly allows... | 2015-06-05T19:54:26Z | null | null | null | Spatial Transformer Networks | ['Max Jaderberg', 'K. Simonyan', 'Andrew Zisserman', 'K. Kavukcuoglu'] | 2,015 | Neural Information Processing Systems | 7,417 | 42 | ['Computer Science', 'Mathematics'] |
1,506.0264 | You Only Look Once: Unified, Real-Time Object Detection | ['Joseph Redmon', 'Santosh Divvala', 'Ross Girshick', 'Ali Farhadi'] | ['cs.CV'] | We present YOLO, a new approach to object detection. Prior work on object
detection repurposes classifiers to perform detection. Instead, we frame object
detection as a regression problem to spatially separated bounding boxes and
associated class probabilities. A single neural network predicts bounding boxes
and class ... | 2015-06-08T19:52:52Z | null | null | null | null | null | null | null | null | null | null |
1,506.03365 | LSUN: Construction of a Large-scale Image Dataset using Deep Learning
with Humans in the Loop | ['Fisher Yu', 'Ari Seff', 'Yinda Zhang', 'Shuran Song', 'Thomas Funkhouser', 'Jianxiong Xiao'] | ['cs.CV'] | While there has been remarkable progress in the performance of visual
recognition algorithms, the state-of-the-art models tend to be exceptionally
data-hungry. Large labeled training datasets, expensive and tedious to produce,
are required to optimize millions of parameters in deep network models. Lagging
behind the gr... | 2015-06-10T15:38:47Z | null | null | null | LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop | ['F. Yu', 'Yinda Zhang', 'Shuran Song', 'Ari Seff', 'Jianxiong Xiao'] | 2,015 | arXiv.org | 2,350 | 28 | ['Computer Science'] |
1,507.05717 | An End-to-End Trainable Neural Network for Image-based Sequence
Recognition and Its Application to Scene Text Recognition | ['Baoguang Shi', 'Xiang Bai', 'Cong Yao'] | ['cs.CV'] | Image-based sequence recognition has been a long-standing research topic in
computer vision. In this paper, we investigate the problem of scene text
recognition, which is among the most important and challenging tasks in
image-based sequence recognition. A novel neural network architecture, which
integrates feature ext... | 2015-07-21T06:26:32Z | 5 figures | null | null | null | null | null | null | null | null | null |
1,508.00305 | Compositional Semantic Parsing on Semi-Structured Tables | ['Panupong Pasupat', 'Percy Liang'] | ['cs.CL'] | Two important aspects of semantic parsing for question answering are the
breadth of the knowledge source and the depth of logical compositionality.
While existing work trades off one aspect for another, this paper
simultaneously makes progress on both fronts through a new task: answering
complex questions on semi-struc... | 2015-08-03T02:53:01Z | null | null | null | null | null | null | null | null | null | null |
1,508.01991 | Bidirectional LSTM-CRF Models for Sequence Tagging | ['Zhiheng Huang', 'Wei Xu', 'Kai Yu'] | ['cs.CL'] | In this paper, we propose a variety of Long Short-Term Memory (LSTM) based
models for sequence tagging. These models include LSTM networks, bidirectional
LSTM (BI-LSTM) networks, LSTM with a Conditional Random Field (CRF) layer
(LSTM-CRF) and bidirectional LSTM with a CRF layer (BI-LSTM-CRF). Our work is
the first to a... | 2015-08-09T06:32:47Z | null | null | null | Bidirectional LSTM-CRF Models for Sequence Tagging | ['Zhiheng Huang', 'W. Xu', 'Kai Yu'] | 2,015 | arXiv.org | 4,042 | 35 | ['Computer Science'] |
1,508.05326 | A large annotated corpus for learning natural language inference | ['Samuel R. Bowman', 'Gabor Angeli', 'Christopher Potts', 'Christopher D. Manning'] | ['cs.CL'] | Understanding entailment and contradiction is fundamental to understanding
natural language, and inference about entailment and contradiction is a
valuable testing ground for the development of semantic representations.
However, machine learning research in this area has been dramatically limited
by the lack of large-s... | 2015-08-21T16:17:01Z | To appear at EMNLP 2015. The data will be posted shortly before the
conference (the week of 14 Sep) at http://nlp.stanford.edu/projects/snli/ | null | null | null | null | null | null | null | null | null |
1,508.07909 | Neural Machine Translation of Rare Words with Subword Units | ['Rico Sennrich', 'Barry Haddow', 'Alexandra Birch'] | ['cs.CL'] | Neural machine translation (NMT) models typically operate with a fixed
vocabulary, but translation is an open-vocabulary problem. Previous work
addresses the translation of out-of-vocabulary words by backing off to a
dictionary. In this paper, we introduce a simpler and more effective approach,
making the NMT model cap... | 2015-08-31T16:37:31Z | accepted at ACL 2016; new in this version: figure 3 | null | null | Neural Machine Translation of Rare Words with Subword Units | ['Rico Sennrich', 'B. Haddow', 'Alexandra Birch'] | 2,015 | Annual Meeting of the Association for Computational Linguistics | 7,779 | 42 | ['Computer Science'] |
1,509.00519 | Importance Weighted Autoencoders | ['Yuri Burda', 'Roger Grosse', 'Ruslan Salakhutdinov'] | ['cs.LG', 'stat.ML'] | The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently
proposed generative model pairing a top-down generative network with a
bottom-up recognition network which approximates posterior inference. It
typically makes strong assumptions about posterior inference, for instance that
the posterior distributi... | 2015-09-01T22:33:13Z | Submitted to ICLR 2015 | null | null | null | null | null | null | null | null | null |
1,510.03055 | A Diversity-Promoting Objective Function for Neural Conversation Models | ['Jiwei Li', 'Michel Galley', 'Chris Brockett', 'Jianfeng Gao', 'Bill Dolan'] | ['cs.CL'] | Sequence-to-sequence neural network models for generation of conversational
responses tend to generate safe, commonplace responses (e.g., "I don't know")
regardless of the input. We suggest that the traditional objective function,
i.e., the likelihood of output (response) given input (message) is unsuited to
response g... | 2015-10-11T14:04:57Z | In. Proc of NAACL 2016 | null | null | A Diversity-Promoting Objective Function for Neural Conversation Models | ['Jiwei Li', 'Michel Galley', 'Chris Brockett', 'Jianfeng Gao', 'W. Dolan'] | 2,015 | North American Chapter of the Association for Computational Linguistics | 2,407 | 49 | ['Computer Science'] |
1,510.08484 | MUSAN: A Music, Speech, and Noise Corpus | ['David Snyder', 'Guoguo Chen', 'Daniel Povey'] | ['cs.SD'] | This report introduces a new corpus of music, speech, and noise. This dataset
is suitable for training models for voice activity detection (VAD) and
music/speech discrimination. Our corpus is released under a flexible Creative
Commons license. The dataset consists of music from several genres, speech from
twelve langua... | 2015-10-28T20:59:04Z | null | null | null | null | null | null | null | null | null | null |
1,511.02283 | Generation and Comprehension of Unambiguous Object Descriptions | ['Junhua Mao', 'Jonathan Huang', 'Alexander Toshev', 'Oana Camburu', 'Alan Yuille', 'Kevin Murphy'] | ['cs.CV', 'cs.CL', 'cs.LG', 'cs.RO', 'I.2.6; I.2.7; I.2.10'] | We propose a method that can generate an unambiguous description (known as a
referring expression) of a specific object or region in an image, and which can
also comprehend or interpret such an expression to infer which object is being
described. We show that our method outperforms previous methods that generate
descri... | 2015-11-07T02:17:36Z | We have released the Google Refexp dataset together with a toolbox
for visualization and evaluation, see
https://github.com/mjhucla/Google_Refexp_toolbox. Camera ready version for
CVPR 2016 | null | null | null | null | null | null | null | null | null |
1,511.03086 | The CTU Prague Relational Learning Repository | ['Jan Motl', 'Oliver Schulte'] | ['cs.LG', 'cs.DB', 'I.2.6; H.2.8'] | The aim of the Prague Relational Learning Repository is to support machine
learning research with multi-relational data. The repository currently contains
148 SQL databases hosted on a public MySQL server located at
https://relational.fel.cvut.cz. The server is provided by the Czech Technical
University (CTU). A search... | 2015-11-10T12:30:42Z | 9 pages | null | null | null | null | null | null | null | null | null |
1,511.06434 | Unsupervised Representation Learning with Deep Convolutional Generative
Adversarial Networks | ['Alec Radford', 'Luke Metz', 'Soumith Chintala'] | ['cs.LG', 'cs.CV'] | In recent years, supervised learning with convolutional networks (CNNs) has
seen huge adoption in computer vision applications. Comparatively, unsupervised
learning with CNNs has received less attention. In this work we hope to help
bridge the gap between the success of CNNs for supervised learning and
unsupervised lea... | 2015-11-19T22:50:32Z | Under review as a conference paper at ICLR 2016 | null | null | null | null | null | null | null | null | null |
1,511.06581 | Dueling Network Architectures for Deep Reinforcement Learning | ['Ziyu Wang', 'Tom Schaul', 'Matteo Hessel', 'Hado van Hasselt', 'Marc Lanctot', 'Nando de Freitas'] | ['cs.LG'] | In recent years there have been many successes of using deep representations
in reinforcement learning. Still, many of these applications use conventional
architectures, such as convolutional networks, LSTMs, or auto-encoders. In this
paper, we present a new neural network architecture for model-free
reinforcement lear... | 2015-11-20T13:07:54Z | 15 pages, 5 figures, and 5 tables | null | null | null | null | null | null | null | null | null |
1,511.09207 | Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015
Robust Reading Competition Challenge 4 | ['Cong Yao', 'Jianan Wu', 'Xinyu Zhou', 'Chi Zhang', 'Shuchang Zhou', 'Zhimin Cao', 'Qi Yin'] | ['cs.CV'] | Different from focused texts present in natural images, which are captured
with user's intention and intervention, incidental texts usually exhibit much
more diversity, variability and complexity, thus posing significant
difficulties and challenges for scene text detection and recognition
algorithms. The ICDAR 2015 Rob... | 2015-11-30T09:08:02Z | 3 pages, 2 figures, 5 tables | null | null | null | null | null | null | null | null | null |
1,512.00567 | Rethinking the Inception Architecture for Computer Vision | ['Christian Szegedy', 'Vincent Vanhoucke', 'Sergey Ioffe', 'Jonathon Shlens', 'Zbigniew Wojna'] | ['cs.CV'] | Convolutional networks are at the core of most state-of-the-art computer
vision solutions for a wide variety of tasks. Since 2014 very deep
convolutional networks started to become mainstream, yielding substantial gains
in various benchmarks. Although increased model size and computational cost
tend to translate to imm... | 2015-12-02T03:44:38Z | null | null | null | null | null | null | null | null | null | null |
1,512.02134 | A Large Dataset to Train Convolutional Networks for Disparity, Optical
Flow, and Scene Flow Estimation | ['Nikolaus Mayer', 'Eddy Ilg', 'Philip Häusser', 'Philipp Fischer', 'Daniel Cremers', 'Alexey Dosovitskiy', 'Thomas Brox'] | ['cs.CV', 'cs.LG', 'stat.ML', 'I.2.6; I.2.10; I.4.8'] | Recent work has shown that optical flow estimation can be formulated as a
supervised learning task and can be successfully solved with convolutional
networks. Training of the so-called FlowNet was enabled by a large
synthetically generated dataset. The present paper extends the concept of
optical flow estimation via co... | 2015-12-07T17:35:00Z | Includes supplementary material | null | 10.1109/CVPR.2016.438 | A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation | ['N. Mayer', 'Eddy Ilg', 'Philip Häusser', 'P. Fischer', 'D. Cremers', 'Alexey Dosovitskiy', 'T. Brox'] | 2,015 | Computer Vision and Pattern Recognition | 2,656 | 30 | ['Computer Science', 'Mathematics'] |
1,512.02325 | SSD: Single Shot MultiBox Detector | ['Wei Liu', 'Dragomir Anguelov', 'Dumitru Erhan', 'Christian Szegedy', 'Scott Reed', 'Cheng-Yang Fu', 'Alexander C. Berg'] | ['cs.CV'] | We present a method for detecting objects in images using a single deep
neural network. Our approach, named SSD, discretizes the output space of
bounding boxes into a set of default boxes over different aspect ratios and
scales per feature map location. At prediction time, the network generates
scores for the presence ... | 2015-12-08T04:46:38Z | ECCV 2016 | null | 10.1007/978-3-319-46448-0_2 | null | null | null | null | null | null | null |
1,512.03385 | Deep Residual Learning for Image Recognition | ['Kaiming He', 'Xiangyu Zhang', 'Shaoqing Ren', 'Jian Sun'] | ['cs.CV'] | Deeper neural networks are more difficult to train. We present a residual
learning framework to ease the training of networks that are substantially
deeper than those used previously. We explicitly reformulate the layers as
learning residual functions with reference to the layer inputs, instead of
learning unreferenced... | 2015-12-10T19:51:55Z | Tech report | null | null | null | null | null | null | null | null | null |
1,602.00134 | Convolutional Pose Machines | ['Shih-En Wei', 'Varun Ramakrishna', 'Takeo Kanade', 'Yaser Sheikh'] | ['cs.CV'] | Pose Machines provide a sequential prediction framework for learning rich
implicit spatial models. In this work we show a systematic design for how
convolutional networks can be incorporated into the pose machine framework for
learning image features and image-dependent spatial models for the task of pose
estimation. T... | 2016-01-30T16:15:28Z | camera ready | null | null | null | null | null | null | null | null | null |
1,602.00763 | Simple Online and Realtime Tracking | ['Alex Bewley', 'Zongyuan Ge', 'Lionel Ott', 'Fabio Ramos', 'Ben Upcroft'] | ['cs.CV'] | This paper explores a pragmatic approach to multiple object tracking where
the main focus is to associate objects efficiently for online and realtime
applications. To this end, detection quality is identified as a key factor
influencing tracking performance, where changing the detector can improve
tracking by up to 18.... | 2016-02-02T01:39:28Z | Presented at ICIP 2016, code is available at
https://github.com/abewley/sort | null | 10.1109/ICIP.2016.7533003 | Simple online and realtime tracking | ['A. Bewley', 'ZongYuan Ge', 'Lionel Ott', 'F. Ramos', 'B. Upcroft'] | 2,016 | International Conference on Information Photonics | 3,127 | 26 | ['Computer Science'] |
1,602.02355 | Hyperparameter optimization with approximate gradient | ['Fabian Pedregosa'] | ['stat.ML', 'cs.LG', 'math.OC'] | Most models in machine learning contain at least one hyperparameter to
control for model complexity. Choosing an appropriate set of hyperparameters is
both crucial in terms of model accuracy and computationally challenging. In
this work we propose an algorithm for the optimization of continuous
hyperparameters using in... | 2016-02-07T10:37:13Z | Fixes error in proof of Theorem 2 | null | null | null | null | null | null | null | null | null |
1,602.02644 | Generating Images with Perceptual Similarity Metrics based on Deep
Networks | ['Alexey Dosovitskiy', 'Thomas Brox'] | ['cs.LG', 'cs.CV', 'cs.NE'] | Image-generating machine learning models are typically trained with loss
functions based on distance in the image space. This often leads to
over-smoothed results. We propose a class of loss functions, which we call deep
perceptual similarity metrics (DeePSiM), that mitigate this problem. Instead of
computing distances... | 2016-02-08T16:50:28Z | minor corrections | null | null | null | null | null | null | null | null | null |
1,602.03012 | EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic
Videos | ['Andru P. Twinanda', 'Sherif Shehata', 'Didier Mutter', 'Jacques Marescaux', 'Michel de Mathelin', 'Nicolas Padoy'] | ['cs.CV'] | Surgical workflow recognition has numerous potential medical applications,
such as the automatic indexing of surgical video databases and the optimization
of real-time operating room scheduling, among others. As a result, phase
recognition has been studied in the context of several kinds of surgeries, such
as cataract,... | 2016-02-09T14:58:12Z | Video: https://www.youtube.com/watch?v=6v0NWrFOUUM | null | null | null | null | null | null | null | null | null |
1,602.06023 | Abstractive Text Summarization Using Sequence-to-Sequence RNNs and
Beyond | ['Ramesh Nallapati', 'Bowen Zhou', 'Cicero Nogueira dos santos', 'Caglar Gulcehre', 'Bing Xiang'] | ['cs.CL'] | In this work, we model abstractive text summarization using Attentional
Encoder-Decoder Recurrent Neural Networks, and show that they achieve
state-of-the-art performance on two different corpora. We propose several novel
models that address critical problems in summarization that are not adequately
modeled by the basi... | 2016-02-19T02:04:18Z | null | The SIGNLL Conference on Computational Natural Language Learning
(CoNLL), 2016 | null | Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond | ['Ramesh Nallapati', 'Bowen Zhou', 'C. D. Santos', 'Çaglar Gülçehre', 'Bing Xiang'] | 2,016 | Conference on Computational Natural Language Learning | 2,569 | 34 | ['Computer Science'] |
1,602.07261 | Inception-v4, Inception-ResNet and the Impact of Residual Connections on
Learning | ['Christian Szegedy', 'Sergey Ioffe', 'Vincent Vanhoucke', 'Alex Alemi'] | ['cs.CV'] | Very deep convolutional networks have been central to the largest advances in
image recognition performance in recent years. One example is the Inception
architecture that has been shown to achieve very good performance at relatively
low computational cost. Recently, the introduction of residual connections in
conjunct... | 2016-02-23T18:44:39Z | null | null | null | Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning | ['Christian Szegedy', 'Sergey Ioffe', 'Vincent Vanhoucke', 'Alexander A. Alemi'] | 2,016 | AAAI Conference on Artificial Intelligence | 14,324 | 23 | ['Computer Science'] |
1,602.0736 | SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB
model size | ['Forrest N. Iandola', 'Song Han', 'Matthew W. Moskewicz', 'Khalid Ashraf', 'William J. Dally', 'Kurt Keutzer'] | ['cs.CV', 'cs.AI'] | Recent research on deep neural networks has focused primarily on improving
accuracy. For a given accuracy level, it is typically possible to identify
multiple DNN architectures that achieve that accuracy level. With equivalent
accuracy, smaller DNN architectures offer at least three advantages: (1)
Smaller DNNs require... | 2016-02-24T00:09:45Z | In ICLR Format | null | null | SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size | ['F. Iandola', 'Matthew W. Moskewicz', 'Khalid Ashraf', 'Song Han', 'W. Dally', 'K. Keutzer'] | 2,016 | arXiv.org | 7,522 | 52 | ['Computer Science'] |
1,603.0136 | Neural Architectures for Named Entity Recognition | ['Guillaume Lample', 'Miguel Ballesteros', 'Sandeep Subramanian', 'Kazuya Kawakami', 'Chris Dyer'] | ['cs.CL'] | State-of-the-art named entity recognition systems rely heavily on
hand-crafted features and domain-specific knowledge in order to learn
effectively from the small, supervised training corpora that are available. In
this paper, we introduce two new neural architectures---one based on
bidirectional LSTMs and conditional ... | 2016-03-04T06:36:29Z | Proceedings of NAACL 2016 | null | null | null | null | null | null | null | null | null |
1,603.05027 | Identity Mappings in Deep Residual Networks | ['Kaiming He', 'Xiangyu Zhang', 'Shaoqing Ren', 'Jian Sun'] | ['cs.CV', 'cs.LG'] | Deep residual networks have emerged as a family of extremely deep
architectures showing compelling accuracy and nice convergence behaviors. In
this paper, we analyze the propagation formulations behind the residual
building blocks, which suggest that the forward and backward signals can be
directly propagated from one ... | 2016-03-16T10:53:56Z | ECCV 2016 camera-ready | null | null | null | null | null | null | null | null | null |
1,603.07396 | A Diagram Is Worth A Dozen Images | ['Aniruddha Kembhavi', 'Mike Salvato', 'Eric Kolve', 'Minjoon Seo', 'Hannaneh Hajishirzi', 'Ali Farhadi'] | ['cs.CV', 'cs.AI'] | Diagrams are common tools for representing complex concepts, relationships
and events, often when it would be difficult to portray the same information
with natural images. Understanding natural images has been extensively studied
in computer vision, while diagram understanding has received little attention.
In this pa... | 2016-03-24T00:02:58Z | null | null | null | null | null | null | null | null | null | null |
1,603.08155 | Perceptual Losses for Real-Time Style Transfer and Super-Resolution | ['Justin Johnson', 'Alexandre Alahi', 'Li Fei-Fei'] | ['cs.CV', 'cs.LG'] | We consider image transformation problems, where an input image is
transformed into an output image. Recent methods for such problems typically
train feed-forward convolutional neural networks using a \emph{per-pixel} loss
between the output and ground-truth images. Parallel work has shown that
high-quality images can ... | 2016-03-27T01:04:27Z | null | null | null | null | null | null | null | null | null | null |
1,603.08983 | Adaptive Computation Time for Recurrent Neural Networks | ['Alex Graves'] | ['cs.NE'] | This paper introduces Adaptive Computation Time (ACT), an algorithm that
allows recurrent neural networks to learn how many computational steps to take
between receiving an input and emitting an output. ACT requires minimal changes
to the network architecture, is deterministic and differentiable, and does not
add any n... | 2016-03-29T22:09:00Z | null | null | null | Adaptive Computation Time for Recurrent Neural Networks | ['Alex Graves'] | 2,016 | arXiv.org | 552 | 38 | ['Computer Science'] |
1,604.06174 | Training Deep Nets with Sublinear Memory Cost | ['Tianqi Chen', 'Bing Xu', 'Chiyuan Zhang', 'Carlos Guestrin'] | ['cs.LG'] | We propose a systematic approach to reduce the memory consumption of deep
neural network training. Specifically, we design an algorithm that costs
O(sqrt(n)) memory to train a n layer network, with only the computational cost
of an extra forward pass per mini-batch. As many of the state-of-the-art models
hit the upper ... | 2016-04-21T04:15:27Z | null | null | null | null | null | null | null | null | null | null |
1,605.0317 | DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation
Model | ['Eldar Insafutdinov', 'Leonid Pishchulin', 'Bjoern Andres', 'Mykhaylo Andriluka', 'Bernt Schiele'] | ['cs.CV'] | The goal of this paper is to advance the state-of-the-art of articulated pose
estimation in scenes with multiple people. To that end we contribute on three
fronts. We propose (1) improved body part detectors that generate effective
bottom-up proposals for body parts; (2) novel image-conditioned pairwise terms
that allo... | 2016-05-10T19:49:40Z | ECCV'16. High-res version at
https://www.d2.mpi-inf.mpg.de/sites/default/files/insafutdinov16arxiv.pdf | null | null | null | null | null | null | null | null | null |
1,605.07146 | Wide Residual Networks | ['Sergey Zagoruyko', 'Nikos Komodakis'] | ['cs.CV', 'cs.LG', 'cs.NE'] | Deep residual networks were shown to be able to scale up to thousands of
layers and still have improving performance. However, each fraction of a
percent of improved accuracy costs nearly doubling the number of layers, and so
training very deep residual networks has a problem of diminishing feature
reuse, which makes t... | 2016-05-23T19:27:13Z | null | null | null | Wide Residual Networks | ['Sergey Zagoruyko', 'N. Komodakis'] | 2,016 | British Machine Vision Conference | 8,017 | 32 | ['Computer Science'] |
1,606.00652 | Death and Suicide in Universal Artificial Intelligence | ['Jarryd Martin', 'Tom Everitt', 'Marcus Hutter'] | ['cs.AI', 'I.2.0; I.2.6'] | Reinforcement learning (RL) is a general paradigm for studying intelligent
behaviour, with applications ranging from artificial intelligence to psychology
and economics. AIXI is a universal solution to the RL problem; it can learn any
computable environment. A technical subtlety of AIXI is that it is defined
using a mi... | 2016-06-02T12:48:39Z | Conference: Artificial General Intelligence (AGI) 2016 13 pages, 2
figures | null | null | Death and Suicide in Universal Artificial Intelligence | ['Jarryd Martin', 'Tom Everitt', 'Marcus Hutter'] | 2,016 | Artificial General Intelligence | 21 | 10 | ['Computer Science'] |
1,606.00915 | DeepLab: Semantic Image Segmentation with Deep Convolutional Nets,
Atrous Convolution, and Fully Connected CRFs | ['Liang-Chieh Chen', 'George Papandreou', 'Iasonas Kokkinos', 'Kevin Murphy', 'Alan L. Yuille'] | ['cs.CV'] | In this work we address the task of semantic image segmentation with Deep
Learning and make three main contributions that are experimentally shown to
have substantial practical merit. First, we highlight convolution with
upsampled filters, or 'atrous convolution', as a powerful tool in dense
prediction tasks. Atrous co... | 2016-06-02T21:52:21Z | Accepted by TPAMI | null | null | null | null | null | null | null | null | null |
1,606.02147 | ENet: A Deep Neural Network Architecture for Real-Time Semantic
Segmentation | ['Adam Paszke', 'Abhishek Chaurasia', 'Sangpil Kim', 'Eugenio Culurciello'] | ['cs.CV'] | The ability to perform pixel-wise semantic segmentation in real-time is of
paramount importance in mobile applications. Recent deep neural networks aimed
at this task have the disadvantage of requiring a large number of floating
point operations and have long run-times that hinder their usability. In this
paper, we pro... | 2016-06-07T14:09:27Z | null | null | null | null | null | null | null | null | null | null |
1,606.03498 | Improved Techniques for Training GANs | ['Tim Salimans', 'Ian Goodfellow', 'Wojciech Zaremba', 'Vicki Cheung', 'Alec Radford', 'Xi Chen'] | ['cs.LG', 'cs.CV', 'cs.NE'] | We present a variety of new architectural features and training procedures
that we apply to the generative adversarial networks (GANs) framework. We focus
on two applications of GANs: semi-supervised learning, and the generation of
images that humans find visually realistic. Unlike most work on generative
models, our p... | 2016-06-10T22:53:35Z | null | null | null | null | null | null | null | null | null | null |
1,606.04853 | The ND-IRIS-0405 Iris Image Dataset | ['Kevin W. Bowyer', 'Patrick J. Flynn'] | ['cs.CV'] | The Computer Vision Research Lab at the University of Notre Dame began
collecting iris images in the spring semester of 2004. The initial data
collections used an LG 2200 iris imaging system for image acquisition. Image
datasets acquired in 2004-2005 at Notre Dame with this LG 2200 have been used
in the ICE 2005 and IC... | 2016-06-15T16:40:51Z | 13 pages, 8 figures | null | null | null | null | null | null | null | null | null |
1,606.0525 | SQuAD: 100,000+ Questions for Machine Comprehension of Text | ['Pranav Rajpurkar', 'Jian Zhang', 'Konstantin Lopyrev', 'Percy Liang'] | ['cs.CL'] | We present the Stanford Question Answering Dataset (SQuAD), a new reading
comprehension dataset consisting of 100,000+ questions posed by crowdworkers on
a set of Wikipedia articles, where the answer to each question is a segment of
text from the corresponding reading passage. We analyze the dataset to
understand the t... | 2016-06-16T16:36:00Z | To appear in Proceedings of the 2016 Conference on Empirical Methods
in Natural Language Processing (EMNLP) | null | null | null | null | null | null | null | null | null |
1,606.0665 | 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation | ['Özgün Çiçek', 'Ahmed Abdulkadir', 'Soeren S. Lienkamp', 'Thomas Brox', 'Olaf Ronneberger'] | ['cs.CV'] | This paper introduces a network for volumetric segmentation that learns from
sparsely annotated volumetric images. We outline two attractive use cases of
this method: (1) In a semi-automated setup, the user annotates some slices in
the volume to be segmented. The network learns from these sparse annotations
and provide... | 2016-06-21T16:42:20Z | Conditionally accepted for MICCAI 2016 | null | null | null | null | null | null | null | null | null |
1,607.00653 | node2vec: Scalable Feature Learning for Networks | ['Aditya Grover', 'Jure Leskovec'] | ['cs.SI', 'cs.LG', 'stat.ML'] | Prediction tasks over nodes and edges in networks require careful effort in
engineering features used by learning algorithms. Recent research in the
broader field of representation learning has led to significant progress in
automating prediction by learning the features themselves. However, present
feature learning ap... | 2016-07-03T16:09:30Z | In Proceedings of the 22nd ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, 2016 | null | null | node2vec: Scalable Feature Learning for Networks | ['Aditya Grover', 'J. Leskovec'] | 2,016 | Knowledge Discovery and Data Mining | 10,974 | 47 | ['Computer Science', 'Mathematics', 'Medicine'] |
1,607.01759 | Bag of Tricks for Efficient Text Classification | ['Armand Joulin', 'Edouard Grave', 'Piotr Bojanowski', 'Tomas Mikolov'] | ['cs.CL'] | This paper explores a simple and efficient baseline for text classification.
Our experiments show that our fast text classifier fastText is often on par
with deep learning classifiers in terms of accuracy, and many orders of
magnitude faster for training and evaluation. We can train fastText on more
than one billion wo... | 2016-07-06T19:40:15Z | null | null | null | null | null | null | null | null | null | null |
1,607.04606 | Enriching Word Vectors with Subword Information | ['Piotr Bojanowski', 'Edouard Grave', 'Armand Joulin', 'Tomas Mikolov'] | ['cs.CL', 'cs.LG'] | Continuous word representations, trained on large unlabeled corpora are
useful for many natural language processing tasks. Popular models that learn
such representations ignore the morphology of words, by assigning a distinct
vector to each word. This is a limitation, especially for languages with large
vocabularies an... | 2016-07-15T18:27:55Z | Accepted to TACL. The two first authors contributed equally | null | null | null | null | null | null | null | null | null |
1,607.0645 | Layer Normalization | ['Jimmy Lei Ba', 'Jamie Ryan Kiros', 'Geoffrey E. Hinton'] | ['stat.ML', 'cs.LG'] | Training state-of-the-art, deep neural networks is computationally expensive.
One way to reduce the training time is to normalize the activities of the
neurons. A recently introduced technique called batch normalization uses the
distribution of the summed input to a neuron over a mini-batch of training
cases to compute... | 2016-07-21T19:57:52Z | null | null | null | null | null | null | null | null | null | null |
1,608.00272 | Modeling Context in Referring Expressions | ['Licheng Yu', 'Patrick Poirson', 'Shan Yang', 'Alexander C. Berg', 'Tamara L. Berg'] | ['cs.CV', 'cs.CL'] | Humans refer to objects in their environments all the time, especially in
dialogue with other people. We explore generating and comprehending natural
language referring expressions for objects in images. In particular, we focus
on incorporating better measures of visual context into referring expression
models and find... | 2016-07-31T22:21:42Z | 19 pages, 6 figures, in ECCV 2016; authors, references and
acknowledgement updated | null | null | null | null | null | null | null | null | null |
1,608.06993 | Densely Connected Convolutional Networks | ['Gao Huang', 'Zhuang Liu', 'Laurens van der Maaten', 'Kilian Q. Weinberger'] | ['cs.CV', 'cs.LG'] | Recent work has shown that convolutional networks can be substantially
deeper, more accurate, and efficient to train if they contain shorter
connections between layers close to the input and those close to the output. In
this paper, we embrace this observation and introduce the Dense Convolutional
Network (DenseNet), w... | 2016-08-25T00:44:55Z | CVPR 2017 | null | null | null | null | null | null | null | null | null |
1,609.04802 | Photo-Realistic Single Image Super-Resolution Using a Generative
Adversarial Network | ['Christian Ledig', 'Lucas Theis', 'Ferenc Huszar', 'Jose Caballero', 'Andrew Cunningham', 'Alejandro Acosta', 'Andrew Aitken', 'Alykhan Tejani', 'Johannes Totz', 'Zehan Wang', 'Wenzhe Shi'] | ['cs.CV', 'stat.ML'] | Despite the breakthroughs in accuracy and speed of single image
super-resolution using faster and deeper convolutional neural networks, one
central problem remains largely unsolved: how do we recover the finer texture
details when we super-resolve at large upscaling factors? The behavior of
optimization-based super-res... | 2016-09-15T19:53:07Z | 19 pages, 15 figures, 2 tables, accepted for oral presentation at
CVPR, main paper + some supplementary material | null | null | null | null | null | null | null | null | null |
1,609.05158 | Real-Time Single Image and Video Super-Resolution Using an Efficient
Sub-Pixel Convolutional Neural Network | ['Wenzhe Shi', 'Jose Caballero', 'Ferenc Huszár', 'Johannes Totz', 'Andrew P. Aitken', 'Rob Bishop', 'Daniel Rueckert', 'Zehan Wang'] | ['cs.CV', 'stat.ML'] | Recently, several models based on deep neural networks have achieved great
success in terms of both reconstruction accuracy and computational performance
for single image super-resolution. In these methods, the low resolution (LR)
input image is upscaled to the high resolution (HR) space using a single
filter, commonly... | 2016-09-16T17:58:14Z | CVPR 2016 paper with updated affiliations and supplemental material,
fixed typo in equation 4 | null | null | null | null | null | null | null | null | null |
1,609.07843 | Pointer Sentinel Mixture Models | ['Stephen Merity', 'Caiming Xiong', 'James Bradbury', 'Richard Socher'] | ['cs.CL', 'cs.AI'] | Recent neural network sequence models with softmax classifiers have achieved
their best language modeling performance only with very large hidden states and
large vocabularies. Even then they struggle to predict rare or unseen words
even if the context makes the prediction unambiguous. We introduce the pointer
sentinel... | 2016-09-26T04:06:13Z | null | null | null | null | null | null | null | null | null | null |
1,609.08144 | Google's Neural Machine Translation System: Bridging the Gap between
Human and Machine Translation | ['Yonghui Wu', 'Mike Schuster', 'Zhifeng Chen', 'Quoc V. Le', 'Mohammad Norouzi', 'Wolfgang Macherey', 'Maxim Krikun', 'Yuan Cao', 'Qin Gao', 'Klaus Macherey', 'Jeff Klingner', 'Apurva Shah', 'Melvin Johnson', 'Xiaobing Liu', 'Łukasz Kaiser', 'Stephan Gouws', 'Yoshikiyo Kato', 'Taku Kudo', 'Hideto Kazawa', 'Keith Steve... | ['cs.CL', 'cs.AI', 'cs.LG'] | Neural Machine Translation (NMT) is an end-to-end learning approach for
automated translation, with the potential to overcome many of the weaknesses of
conventional phrase-based translation systems. Unfortunately, NMT systems are
known to be computationally expensive both in training and in translation
inference. Also,... | 2016-09-26T19:59:55Z | null | null | null | null | null | null | null | null | null | null |
1,610.02357 | Xception: Deep Learning with Depthwise Separable Convolutions | ['François Chollet'] | ['cs.CV'] | We present an interpretation of Inception modules in convolutional neural
networks as being an intermediate step in-between regular convolution and the
depthwise separable convolution operation (a depthwise convolution followed by
a pointwise convolution). In this light, a depthwise separable convolution can
be underst... | 2016-10-07T17:51:51Z | null | null | null | null | null | null | null | null | null | null |
1,610.02424 | Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence
Models | ['Ashwin K Vijayakumar', 'Michael Cogswell', 'Ramprasath R. Selvaraju', 'Qing Sun', 'Stefan Lee', 'David Crandall', 'Dhruv Batra'] | ['cs.AI', 'cs.CL', 'cs.CV'] | Neural sequence models are widely used to model time-series data. Equally
ubiquitous is the usage of beam search (BS) as an approximate inference
algorithm to decode output sequences from these models. BS explores the search
space in a greedy left-right fashion retaining only the top-B candidates -
resulting in sequenc... | 2016-10-07T20:56:47Z | 16 pages; accepted at AAAI 2018 | null | null | null | null | null | null | null | null | null |
1,611.01734 | Deep Biaffine Attention for Neural Dependency Parsing | ['Timothy Dozat', 'Christopher D. Manning'] | ['cs.CL', 'cs.NE'] | This paper builds off recent work from Kiperwasser & Goldberg (2016) using
neural attention in a simple graph-based dependency parser. We use a larger but
more thoroughly regularized parser than other recent BiLSTM-based approaches,
with biaffine classifiers to predict arcs and labels. Our parser gets state of
the art ... | 2016-11-06T07:26:38Z | Accepted to ICLR 2017; updated with new results and comparison to
more recent models, including current state-of-the-art | null | null | null | null | null | null | null | null | null |
1,611.022 | Unsupervised Cross-Domain Image Generation | ['Yaniv Taigman', 'Adam Polyak', 'Lior Wolf'] | ['cs.CV'] | We study the problem of transferring a sample in one domain to an analog
sample in another domain. Given two related domains, S and T, we would like to
learn a generative function G that maps an input sample from S to the domain T,
such that the output of a given function f, which accepts inputs in either
domains, woul... | 2016-11-07T18:14:57Z | null | null | null | Unsupervised Cross-Domain Image Generation | ['Yaniv Taigman', 'Adam Polyak', 'Lior Wolf'] | 2,016 | International Conference on Learning Representations | 1,003 | 30 | ['Computer Science'] |
1,611.04033 | 1.5 billion words Arabic Corpus | ['Ibrahim Abu El-khair'] | ['cs.CL', 'cs.DL', 'cs.IR'] | This study is an attempt to build a contemporary linguistic corpus for Arabic
language. The corpus produced, is a text corpus includes more than five million
newspaper articles. It contains over a billion and a half words in total, out
of which, there is about three million unique words. The data were collected
from ne... | 2016-11-12T18:41:58Z | null | null | null | 1.5 billion words Arabic Corpus | ['I. A. El-Khair'] | 2,016 | arXiv.org | 99 | 30 | ['Computer Science'] |
1,611.05431 | Aggregated Residual Transformations for Deep Neural Networks | ['Saining Xie', 'Ross Girshick', 'Piotr Dollár', 'Zhuowen Tu', 'Kaiming He'] | ['cs.CV'] | We present a simple, highly modularized network architecture for image
classification. Our network is constructed by repeating a building block that
aggregates a set of transformations with the same topology. Our simple design
results in a homogeneous, multi-branch architecture that has only a few
hyper-parameters to s... | 2016-11-16T20:34:42Z | Accepted to CVPR 2017. Code and models:
https://github.com/facebookresearch/ResNeXt | null | null | null | null | null | null | null | null | null |
1,611.06455 | Time Series Classification from Scratch with Deep Neural Networks: A
Strong Baseline | ['Zhiguang Wang', 'Weizhong Yan', 'Tim Oates'] | ['cs.LG', 'cs.NE', 'stat.ML'] | We propose a simple but strong baseline for time series classification from
scratch with deep neural networks. Our proposed baseline models are pure
end-to-end without any heavy preprocessing on the raw data or feature crafting.
The proposed Fully Convolutional Network (FCN) achieves premium performance to
other state-... | 2016-11-20T00:34:09Z | null | null | null | null | null | null | null | null | null | null |
1,611.07004 | Image-to-Image Translation with Conditional Adversarial Networks | ['Phillip Isola', 'Jun-Yan Zhu', 'Tinghui Zhou', 'Alexei A. Efros'] | ['cs.CV'] | We investigate conditional adversarial networks as a general-purpose solution
to image-to-image translation problems. These networks not only learn the
mapping from input image to output image, but also learn a loss function to
train this mapping. This makes it possible to apply the same generic approach
to problems th... | 2016-11-21T20:48:16Z | Website: https://phillipi.github.io/pix2pix/, CVPR 2017 | null | null | Image-to-Image Translation with Conditional Adversarial Networks | ['Phillip Isola', 'Jun-Yan Zhu', 'Tinghui Zhou', 'Alexei A. Efros'] | 2,016 | Computer Vision and Pattern Recognition | 19,761 | 70 | ['Computer Science'] |
1,611.07308 | Variational Graph Auto-Encoders | ['Thomas N. Kipf', 'Max Welling'] | ['stat.ML', 'cs.LG'] | We introduce the variational graph auto-encoder (VGAE), a framework for
unsupervised learning on graph-structured data based on the variational
auto-encoder (VAE). This model makes use of latent variables and is capable of
learning interpretable latent representations for undirected graphs. We
demonstrate this model us... | 2016-11-21T11:37:17Z | Bayesian Deep Learning Workshop (NIPS 2016) | null | null | null | null | null | null | null | null | null |
1,611.0805 | Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields | ['Zhe Cao', 'Tomas Simon', 'Shih-En Wei', 'Yaser Sheikh'] | ['cs.CV'] | We present an approach to efficiently detect the 2D pose of multiple people
in an image. The approach uses a nonparametric representation, which we refer
to as Part Affinity Fields (PAFs), to learn to associate body parts with
individuals in the image. The architecture encodes global context, allowing a
greedy bottom-u... | 2016-11-24T01:58:16Z | Accepted as CVPR 2017 Oral. Video result:
https://youtu.be/pW6nZXeWlGM | null | null | Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields | ['Zhe Cao', 'T. Simon', 'S. Wei', 'Yaser Sheikh'] | 2,016 | Computer Vision and Pattern Recognition | 6,570 | 43 | ['Computer Science'] |
1,611.09268 | MS MARCO: A Human Generated MAchine Reading COmprehension Dataset | ['Payal Bajaj', 'Daniel Campos', 'Nick Craswell', 'Li Deng', 'Jianfeng Gao', 'Xiaodong Liu', 'Rangan Majumder', 'Andrew McNamara', 'Bhaskar Mitra', 'Tri Nguyen', 'Mir Rosenberg', 'Xia Song', 'Alina Stoica', 'Saurabh Tiwary', 'Tong Wang'] | ['cs.CL', 'cs.IR'] | We introduce a large scale MAchine Reading COmprehension dataset, which we
name MS MARCO. The dataset comprises of 1,010,916 anonymized
questions---sampled from Bing's search query logs---each with a human generated
answer and 182,669 completely human rewritten generated answers. In addition,
the dataset contains 8,841... | 2016-11-28T18:14:11Z | null | null | null | null | null | null | null | null | null | null |
1,611.10012 | Speed/accuracy trade-offs for modern convolutional object detectors | ['Jonathan Huang', 'Vivek Rathod', 'Chen Sun', 'Menglong Zhu', 'Anoop Korattikara', 'Alireza Fathi', 'Ian Fischer', 'Zbigniew Wojna', 'Yang Song', 'Sergio Guadarrama', 'Kevin Murphy'] | ['cs.CV'] | The goal of this paper is to serve as a guide for selecting a detection
architecture that achieves the right speed/memory/accuracy balance for a given
application and platform. To this end, we investigate various ways to trade
accuracy for speed and memory usage in modern convolutional object detection
systems. A numbe... | 2016-11-30T06:06:15Z | Accepted to CVPR 2017 | null | null | null | null | null | null | null | null | null |
1,612.00496 | 3D Bounding Box Estimation Using Deep Learning and Geometry | ['Arsalan Mousavian', 'Dragomir Anguelov', 'John Flynn', 'Jana Kosecka'] | ['cs.CV'] | We present a method for 3D object detection and pose estimation from a single
image. In contrast to current techniques that only regress the 3D orientation
of an object, our method first regresses relatively stable 3D object properties
using a deep convolutional neural network and then combines these estimates
with geo... | 2016-12-01T22:16:48Z | To appear in IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 2017 | null | null | null | null | null | null | null | null | null |
1,612.00593 | PointNet: Deep Learning on Point Sets for 3D Classification and
Segmentation | ['Charles R. Qi', 'Hao Su', 'Kaichun Mo', 'Leonidas J. Guibas'] | ['cs.CV'] | Point cloud is an important type of geometric data structure. Due to its
irregular format, most researchers transform such data to regular 3D voxel
grids or collections of images. This, however, renders data unnecessarily
voluminous and causes issues. In this paper, we design a novel type of neural
network that directl... | 2016-12-02T08:40:40Z | CVPR 2017 | null | null | null | null | null | null | null | null | null |
1,612.00796 | Overcoming catastrophic forgetting in neural networks | ['James Kirkpatrick', 'Razvan Pascanu', 'Neil Rabinowitz', 'Joel Veness', 'Guillaume Desjardins', 'Andrei A. Rusu', 'Kieran Milan', 'John Quan', 'Tiago Ramalho', 'Agnieszka Grabska-Barwinska', 'Demis Hassabis', 'Claudia Clopath', 'Dharshan Kumaran', 'Raia Hadsell'] | ['cs.LG', 'cs.AI', 'stat.ML'] | The ability to learn tasks in a sequential fashion is crucial to the
development of artificial intelligence. Neural networks are not, in general,
capable of this and it has been widely thought that catastrophic forgetting is
an inevitable feature of connectionist models. We show that it is possible to
overcome this lim... | 2016-12-02T19:18:37Z | null | null | 10.1073/pnas.1611835114 | null | null | null | null | null | null | null |
1,612.0184 | FMA: A Dataset For Music Analysis | ['Michaël Defferrard', 'Kirell Benzi', 'Pierre Vandergheynst', 'Xavier Bresson'] | ['cs.SD', 'cs.IR'] | We introduce the Free Music Archive (FMA), an open and easily accessible
dataset suitable for evaluating several tasks in MIR, a field concerned with
browsing, searching, and organizing large music collections. The community's
growing interest in feature and end-to-end learning is however restrained by
the limited avai... | 2016-12-06T14:58:59Z | ISMIR 2017 camera-ready | null | null | null | null | null | null | null | null | null |
1,612.03144 | Feature Pyramid Networks for Object Detection | ['Tsung-Yi Lin', 'Piotr Dollár', 'Ross Girshick', 'Kaiming He', 'Bharath Hariharan', 'Serge Belongie'] | ['cs.CV'] | Feature pyramids are a basic component in recognition systems for detecting
objects at different scales. But recent deep learning object detectors have
avoided pyramid representations, in part because they are compute and memory
intensive. In this paper, we exploit the inherent multi-scale, pyramidal
hierarchy of deep ... | 2016-12-09T19:55:54Z | null | null | null | null | null | null | null | null | null | null |
1,612.03651 | FastText.zip: Compressing text classification models | ['Armand Joulin', 'Edouard Grave', 'Piotr Bojanowski', 'Matthijs Douze', 'Hérve Jégou', 'Tomas Mikolov'] | ['cs.CL', 'cs.LG'] | We consider the problem of producing compact architectures for text
classification, such that the full model fits in a limited amount of memory.
After considering different solutions inspired by the hashing literature, we
propose a method built upon product quantization to store word embeddings.
While the original tech... | 2016-12-12T12:51:03Z | Submitted to ICLR 2017 | null | null | FastText.zip: Compressing text classification models | ['Armand Joulin', 'Edouard Grave', 'Piotr Bojanowski', 'Matthijs Douze', 'H. Jégou', 'Tomas Mikolov'] | 2,016 | arXiv.org | 1,216 | 45 | ['Computer Science'] |
1,612.06321 | Large-Scale Image Retrieval with Attentive Deep Local Features | ['Hyeonwoo Noh', 'Andre Araujo', 'Jack Sim', 'Tobias Weyand', 'Bohyung Han'] | ['cs.CV'] | We propose an attentive local feature descriptor suitable for large-scale
image retrieval, referred to as DELF (DEep Local Feature). The new feature is
based on convolutional neural networks, which are trained only with image-level
annotations on a landmark image dataset. To identify semantically useful local
features ... | 2016-12-19T19:35:56Z | ICCV 2017. Code and dataset available:
https://github.com/tensorflow/models/tree/master/research/delf | null | null | null | null | null | null | null | null | null |
1,612.07695 | MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving | ['Marvin Teichmann', 'Michael Weber', 'Marius Zoellner', 'Roberto Cipolla', 'Raquel Urtasun'] | ['cs.CV', 'cs.RO'] | While most approaches to semantic reasoning have focused on improving
performance, in this paper we argue that computational times are very important
in order to enable real time applications such as autonomous driving. Towards
this goal, we present an approach to joint classification, detection and
semantic segmentati... | 2016-12-22T16:55:02Z | 9 pages, 7 tables and 9 figures; first place on Kitti Road
Segmentation; Code on GitHub (https://github.com/MarvinTeichmann/MultiNet) | null | null | MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving | ['Marvin Teichmann', 'Michael Weber', 'Johann Marius Zöllner', 'R. Cipolla', 'R. Urtasun'] | 2,016 | 2018 IEEE Intelligent Vehicles Symposium (IV) | 702 | 68 | ['Computer Science'] |
1,612.08083 | Language Modeling with Gated Convolutional Networks | ['Yann N. Dauphin', 'Angela Fan', 'Michael Auli', 'David Grangier'] | ['cs.CL'] | The pre-dominant approach to language modeling to date is based on recurrent
neural networks. Their success on this task is often linked to their ability to
capture unbounded context. In this paper we develop a finite context approach
through stacked convolutions, which can be more efficient since they allow
paralleliz... | 2016-12-23T20:32:33Z | null | null | null | null | null | null | null | null | null | null |
1,612.08242 | YOLO9000: Better, Faster, Stronger | ['Joseph Redmon', 'Ali Farhadi'] | ['cs.CV'] | We introduce YOLO9000, a state-of-the-art, real-time object detection system
that can detect over 9000 object categories. First we propose various
improvements to the YOLO detection method, both novel and drawn from prior
work. The improved model, YOLOv2, is state-of-the-art on standard detection
tasks like PASCAL VOC ... | 2016-12-25T07:21:38Z | null | null | null | YOLO9000: Better, Faster, Stronger | ['Joseph Redmon', 'Ali Farhadi'] | 2,016 | Computer Vision and Pattern Recognition | 15,699 | 20 | ['Computer Science'] |
1,701.02718 | See the Glass Half Full: Reasoning about Liquid Containers, their Volume
and Content | ['Roozbeh Mottaghi', 'Connor Schenck', 'Dieter Fox', 'Ali Farhadi'] | ['cs.CV'] | Humans have rich understanding of liquid containers and their contents; for
example, we can effortlessly pour water from a pitcher to a cup. Doing so
requires estimating the volume of the cup, approximating the amount of water in
the pitcher, and predicting the behavior of water when we tilt the pitcher.
Very little at... | 2017-01-10T18:25:15Z | null | null | null | null | null | null | null | null | null | null |
1,701.03755 | What Can I Do Now? Guiding Users in a World of Automated Decisions | ['Matthias Gallé'] | ['stat.ML'] | More and more processes governing our lives use in some part an automatic
decision step, where -- based on a feature vector derived from an applicant --
an algorithm has the decision power over the final outcome. Here we present a
simple idea which gives some of the power back to the applicant by providing
her with alt... | 2017-01-13T17:49:47Z | presented at BigIA 2016 workshop: http://bigia2016.irisa.fr/ | null | null | What Can I Do Now? Guiding Users in a World of Automated Decisions | ['Matthias Gallé'] | 2,017 | null | 0 | 13 | ['Mathematics', 'Computer Science'] |
1,701.06538 | Outrageously Large Neural Networks: The Sparsely-Gated
Mixture-of-Experts Layer | ['Noam Shazeer', 'Azalia Mirhoseini', 'Krzysztof Maziarz', 'Andy Davis', 'Quoc Le', 'Geoffrey Hinton', 'Jeff Dean'] | ['cs.LG', 'cs.CL', 'cs.NE', 'stat.ML'] | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice... | 2017-01-23T18:10:00Z | null | null | null | null | null | null | null | null | null | null |
1,701.07875 | Wasserstein GAN | ['Martin Arjovsky', 'Soumith Chintala', 'Léon Bottou'] | ['stat.ML', 'cs.LG'] | We introduce a new algorithm named WGAN, an alternative to traditional GAN
training. In this new model, we show that we can improve the stability of
learning, get rid of problems like mode collapse, and provide meaningful
learning curves useful for debugging and hyperparameter searches. Furthermore,
we show that the co... | 2017-01-26T21:10:29Z | null | null | null | Wasserstein GAN | ['Martín Arjovsky', 'Soumith Chintala', 'Léon Bottou'] | 2,017 | arXiv.org | 4,837 | 26 | ['Mathematics', 'Computer Science'] |
1,701.08071 | Emotion Recognition From Speech With Recurrent Neural Networks | ['Vladimir Chernykh', 'Pavel Prikhodko'] | ['cs.CL'] | In this paper the task of emotion recognition from speech is considered.
Proposed approach uses deep recurrent neural network trained on a sequence of
acoustic features calculated over small speech intervals. At the same time
special probabilistic-nature CTC loss function allows to consider long
utterances containing b... | 2017-01-27T14:50:36Z | null | null | null | Emotion Recognition From Speech With Recurrent Neural Networks | ['V. Chernykh', 'Grigoriy Sterling', 'Pavel Prihodko'] | 2,017 | arXiv.org | 117 | 11 | ['Computer Science'] |
1,701.08118 | Measuring the Reliability of Hate Speech Annotations: The Case of the
European Refugee Crisis | ['Björn Ross', 'Michael Rist', 'Guillermo Carbonell', 'Benjamin Cabrera', 'Nils Kurowsky', 'Michael Wojatzki'] | ['cs.CL'] | Some users of social media are spreading racist, sexist, and otherwise
hateful content. For the purpose of training a hate speech detection system,
the reliability of the annotations is crucial, but there is no universally
agreed-upon definition. We collected potentially hateful messages and asked two
groups of interne... | 2017-01-27T17:09:07Z | null | Proceedings of NLP4CMC III: 3rd Workshop on Natural Language
Processing for Computer-Mediated Communication (Bochum), Bochumer
Linguistische Arbeitsberichte, vol. 17, sep 2016, pp. 6-9 | 10.17185/duepublico/42132 | null | null | null | null | null | null | null |
1,702.00992 | Automatic Prediction of Discourse Connectives | ['Eric Malmi', 'Daniele Pighin', 'Sebastian Krause', 'Mikhail Kozhevnikov'] | ['cs.CL'] | Accurate prediction of suitable discourse connectives (however, furthermore,
etc.) is a key component of any system aimed at building coherent and fluent
discourses from shorter sentences and passages. As an example, a dialog system
might assemble a long and informative answer by sampling passages extracted
from differ... | 2017-02-03T13:06:25Z | This is a pre-print of an article appearing at LREC 2018 | null | null | null | null | null | null | null | null | null |
1,702.04066 | JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction | ['Courtney Napoles', 'Keisuke Sakaguchi', 'Joel Tetreault'] | ['cs.CL'] | We present a new parallel corpus, JHU FLuency-Extended GUG corpus (JFLEG) for
developing and evaluating grammatical error correction (GEC). Unlike other
corpora, it represents a broad range of language proficiency levels and uses
holistic fluency edits to not only correct grammatical errors but also make the
original t... | 2017-02-14T03:47:34Z | To appear in EACL 2017 (short papers) | null | null | null | null | null | null | null | null | null |
1,702.05373 | EMNIST: an extension of MNIST to handwritten letters | ['Gregory Cohen', 'Saeed Afshar', 'Jonathan Tapson', 'André van Schaik'] | ['cs.CV'] | The MNIST dataset has become a standard benchmark for learning,
classification and computer vision systems. Contributing to its widespread
adoption are the understandable and intuitive nature of the task, its
relatively small size and storage requirements and the accessibility and
ease-of-use of the database itself. Th... | 2017-02-17T15:06:14Z | The dataset is now available for download from
https://www.westernsydney.edu.au/bens/home/reproducible_research/emnist. This
link is also included in the revised article | null | null | null | null | null | null | null | null | null |
1,702.08734 | Billion-scale similarity search with GPUs | ['Jeff Johnson', 'Matthijs Douze', 'Hervé Jégou'] | ['cs.CV', 'cs.DB', 'cs.DS', 'cs.IR'] | Similarity search finds application in specialized database systems handling
complex data such as images or videos, which are typically represented by
high-dimensional features and require specific indexing structures. This paper
tackles the problem of better utilizing GPUs for this task. While GPUs excel at
data-paral... | 2017-02-28T10:42:31Z | null | null | null | null | null | null | null | null | null | null |
1,703.01365 | Axiomatic Attribution for Deep Networks | ['Mukund Sundararajan', 'Ankur Taly', 'Qiqi Yan'] | ['cs.LG'] | We study the problem of attributing the prediction of a deep network to its
input features, a problem previously studied by several other works. We
identify two fundamental axioms---Sensitivity and Implementation Invariance
that attribution methods ought to satisfy. We show that they are not satisfied
by most known att... | 2017-03-04T00:18:49Z | null | null | null | Axiomatic Attribution for Deep Networks | ['Mukund Sundararajan', 'Ankur Taly', 'Qiqi Yan'] | 2,017 | International Conference on Machine Learning | 6,065 | 35 | ['Computer Science'] |
1,703.034 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | ['Chelsea Finn', 'Pieter Abbeel', 'Sergey Levine'] | ['cs.LG', 'cs.AI', 'cs.CV', 'cs.NE'] | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on... | 2017-03-09T18:58:03Z | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | null | null | null | null | null | null | null | null |
1,703.04009 | Automated Hate Speech Detection and the Problem of Offensive Language | ['Thomas Davidson', 'Dana Warmsley', 'Michael Macy', 'Ingmar Weber'] | ['cs.CL'] | A key challenge for automatic hate-speech detection on social media is the
separation of hate speech from other instances of offensive language. Lexical
detection methods tend to have low precision because they classify all messages
containing particular terms as hate speech and previous work using supervised
learning ... | 2017-03-11T18:20:13Z | To appear in the Proceedings of ICWSM 2017. Please cite that version | null | null | null | null | null | null | null | null | null |
1,703.05175 | Prototypical Networks for Few-shot Learning | ['Jake Snell', 'Kevin Swersky', 'Richard S. Zemel'] | ['cs.LG', 'stat.ML'] | We propose prototypical networks for the problem of few-shot classification,
where a classifier must generalize to new classes not seen in the training set,
given only a small number of examples of each new class. Prototypical networks
learn a metric space in which classification can be performed by computing
distances... | 2017-03-15T14:31:55Z | null | null | null | null | null | null | null | null | null | null |
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