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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,703.06103 | Modeling Relational Data with Graph Convolutional Networks | ['Michael Schlichtkrull', 'Thomas N. Kipf', 'Peter Bloem', 'Rianne van den Berg', 'Ivan Titov', 'Max Welling'] | ['stat.ML', 'cs.AI', 'cs.DB', 'cs.LG'] | Knowledge graphs enable a wide variety of applications, including question
answering and information retrieval. Despite the great effort invested in their
creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata)
remain incomplete. We introduce Relational Graph Convolutional Networks
(R-GCNs) and app... | 2017-03-17T17:09:14Z | null | null | null | Modeling Relational Data with Graph Convolutional Networks | ['M. Schlichtkrull', 'Thomas Kipf', 'Peter Bloem', 'Rianne van den Berg', 'Ivan Titov', 'M. Welling'] | 2,017 | Extended Semantic Web Conference | 4,866 | 54 | ['Computer Science', 'Mathematics'] |
1,703.0687 | Mask R-CNN | ['Kaiming He', 'Georgia Gkioxari', 'Piotr Dollár', 'Ross Girshick'] | ['cs.CV'] | We present a conceptually simple, flexible, and general framework for object
instance segmentation. Our approach efficiently detects objects in an image
while simultaneously generating a high-quality segmentation mask for each
instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a
branch for predicti... | 2017-03-20T17:53:38Z | open source; appendix on more results | null | null | null | null | null | null | null | null | null |
1,703.07469 | RobustFill: Neural Program Learning under Noisy I/O | ['Jacob Devlin', 'Jonathan Uesato', 'Surya Bhupatiraju', 'Rishabh Singh', 'Abdel-rahman Mohamed', 'Pushmeet Kohli'] | ['cs.AI'] | The problem of automatically generating a computer program from some
specification has been studied since the early days of AI. Recently, two
competing approaches for automatic program learning have received significant
attention: (1) neural program synthesis, where a neural network is conditioned
on input/output (I/O)... | 2017-03-21T23:29:47Z | 8 pages + 9 pages of supplementary material | null | null | null | null | null | null | null | null | null |
1,703.07737 | In Defense of the Triplet Loss for Person Re-Identification | ['Alexander Hermans', 'Lucas Beyer', 'Bastian Leibe'] | ['cs.CV', 'cs.NE'] | In the past few years, the field of computer vision has gone through a
revolution fueled mainly by the advent of large datasets and the adoption of
deep convolutional neural networks for end-to-end learning. The person
re-identification subfield is no exception to this. Unfortunately, a prevailing
belief in the communi... | 2017-03-22T16:34:29Z | Lucas Beyer and Alexander Hermans contributed equally. Updates: Minor
fixes, new SOTA comparisons, add CUHK03 results | null | null | In Defense of the Triplet Loss for Person Re-Identification | ['Alexander Hermans', 'Lucas Beyer', 'B. Leibe'] | 2,017 | arXiv.org | 3,221 | 56 | ['Computer Science'] |
1,703.10135 | Tacotron: Towards End-to-End Speech Synthesis | ['Yuxuan Wang', 'RJ Skerry-Ryan', 'Daisy Stanton', 'Yonghui Wu', 'Ron J. Weiss', 'Navdeep Jaitly', 'Zongheng Yang', 'Ying Xiao', 'Zhifeng Chen', 'Samy Bengio', 'Quoc Le', 'Yannis Agiomyrgiannakis', 'Rob Clark', 'Rif A. Saurous'] | ['cs.CL', 'cs.LG', 'cs.SD'] | A text-to-speech synthesis system typically consists of multiple stages, such
as a text analysis frontend, an acoustic model and an audio synthesis module.
Building these components often requires extensive domain expertise and may
contain brittle design choices. In this paper, we present Tacotron, an
end-to-end genera... | 2017-03-29T16:55:13Z | Submitted to Interspeech 2017. v2 changed paper title to be
consistent with our conference submission (no content change other than typo
fixes) | null | null | null | null | null | null | null | null | null |
1,703.10593 | Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial
Networks | ['Jun-Yan Zhu', 'Taesung Park', 'Phillip Isola', 'Alexei A. Efros'] | ['cs.CV'] | Image-to-image translation is a class of vision and graphics problems where
the goal is to learn the mapping between an input image and an output image
using a training set of aligned image pairs. However, for many tasks, paired
training data will not be available. We present an approach for learning to
translate an im... | 2017-03-30T17:44:17Z | An extended version of our ICCV 2017 paper, v7 fixed the typos and
updated the implementation details. Code and data:
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix | null | null | null | null | null | null | null | null | null |
1,704.00028 | Improved Training of Wasserstein GANs | ['Ishaan Gulrajani', 'Faruk Ahmed', 'Martin Arjovsky', 'Vincent Dumoulin', 'Aaron Courville'] | ['cs.LG', 'stat.ML'] | Generative Adversarial Networks (GANs) are powerful generative models, but
suffer from training instability. The recently proposed Wasserstein GAN (WGAN)
makes progress toward stable training of GANs, but sometimes can still generate
only low-quality samples or fail to converge. We find that these problems are
often du... | 2017-03-31T19:25:00Z | NIPS camera-ready | null | null | Improved Training of Wasserstein GANs | ['Ishaan Gulrajani', 'Faruk Ahmed', 'Martín Arjovsky', 'Vincent Dumoulin', 'Aaron C. Courville'] | 2,017 | Neural Information Processing Systems | 9,605 | 37 | ['Mathematics', 'Computer Science'] |
1,704.02853 | SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations
from Scientific Publications | ['Isabelle Augenstein', 'Mrinal Das', 'Sebastian Riedel', 'Lakshmi Vikraman', 'Andrew McCallum'] | ['cs.CL', 'cs.AI', 'stat.ML'] | We describe the SemEval task of extracting keyphrases and relations between
them from scientific documents, which is crucial for understanding which
publications describe which processes, tasks and materials. Although this was a
new task, we had a total of 26 submissions across 3 evaluation scenarios. We
expect the tas... | 2017-04-10T13:43:40Z | null | null | null | null | null | null | null | null | null | null |
1,704.03155 | EAST: An Efficient and Accurate Scene Text Detector | ['Xinyu Zhou', 'Cong Yao', 'He Wen', 'Yuzhi Wang', 'Shuchang Zhou', 'Weiran He', 'Jiajun Liang'] | ['cs.CV'] | Previous approaches for scene text detection have already achieved promising
performances across various benchmarks. However, they usually fall short when
dealing with challenging scenarios, even when equipped with deep neural network
models, because the overall performance is determined by the interplay of
multiple st... | 2017-04-11T06:04:12Z | Accepted to CVPR 2017, fix equation (3) | null | null | EAST: An Efficient and Accurate Scene Text Detector | ['Xinyu Zhou', 'C. Yao', 'He Wen', 'Yuzhi Wang', 'Shuchang Zhou', 'Weiran He', 'Jiajun Liang'] | 2,017 | Computer Vision and Pattern Recognition | 1,499 | 50 | ['Computer Science'] |
1,704.04086 | Beyond Face Rotation: Global and Local Perception GAN for Photorealistic
and Identity Preserving Frontal View Synthesis | ['Rui Huang', 'Shu Zhang', 'Tianyu Li', 'Ran He'] | ['cs.CV'] | Photorealistic frontal view synthesis from a single face image has a wide
range of applications in the field of face recognition. Although data-driven
deep learning methods have been proposed to address this problem by seeking
solutions from ample face data, this problem is still challenging because it is
intrinsically... | 2017-04-13T12:18:13Z | accepted at ICCV 2017, main paper & supplementary material, 11 pages | null | null | null | null | null | null | null | null | null |
1,704.04503 | Soft-NMS -- Improving Object Detection With One Line of Code | ['Navaneeth Bodla', 'Bharat Singh', 'Rama Chellappa', 'Larry S. Davis'] | ['cs.CV'] | Non-maximum suppression is an integral part of the object detection pipeline.
First, it sorts all detection boxes on the basis of their scores. The detection
box M with the maximum score is selected and all other detection boxes with a
significant overlap (using a pre-defined threshold) with M are suppressed. This
proc... | 2017-04-14T18:00:03Z | ICCV 2017 camera ready version | null | null | Soft-NMS — Improving Object Detection with One Line of Code | ['Navaneeth Bodla', 'Bharat Singh', 'R. Chellappa', 'L. Davis'] | 2,017 | IEEE International Conference on Computer Vision | 1,801 | 33 | ['Computer Science'] |
1,704.04861 | MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
Applications | ['Andrew G. Howard', 'Menglong Zhu', 'Bo Chen', 'Dmitry Kalenichenko', 'Weijun Wang', 'Tobias Weyand', 'Marco Andreetto', 'Hartwig Adam'] | ['cs.CV'] | We present a class of efficient models called MobileNets for mobile and
embedded vision applications. MobileNets are based on a streamlined
architecture that uses depth-wise separable convolutions to build light weight
deep neural networks. We introduce two simple global hyper-parameters that
efficiently trade off betw... | 2017-04-17T03:57:34Z | null | null | null | MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications | ['Andrew G. Howard', 'Menglong Zhu', 'Bo Chen', 'Dmitry Kalenichenko', 'Weijun Wang', 'Tobias Weyand', 'M. Andreetto', 'Hartwig Adam'] | 2,017 | arXiv.org | 20,998 | 38 | ['Computer Science'] |
1,704.05179 | SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine | ['Matthew Dunn', 'Levent Sagun', 'Mike Higgins', 'V. Ugur Guney', 'Volkan Cirik', 'Kyunghyun Cho'] | ['cs.CL'] | We publicly release a new large-scale dataset, called SearchQA, for machine
comprehension, or question-answering. Unlike recently released datasets, such
as DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to
reflect a full pipeline of general question-answering. That is, we start not
from an exi... | 2017-04-18T02:42:17Z | null | null | null | null | null | null | null | null | null | null |
1,704.05426 | A Broad-Coverage Challenge Corpus for Sentence Understanding through
Inference | ['Adina Williams', 'Nikita Nangia', 'Samuel R. Bowman'] | ['cs.CL'] | This paper introduces the Multi-Genre Natural Language Inference (MultiNLI)
corpus, a dataset designed for use in the development and evaluation of machine
learning models for sentence understanding. In addition to being one of the
largest corpora available for the task of NLI, at 433k examples, this corpus
improves up... | 2017-04-18T17:10:13Z | 10 pages, 1 figures, 5 tables. v2 corrects a misreported accuracy
number for the CBOW model in the 'matched' setting. v3 adds a discussion of
the difficulty of the corpus to the analysis section. v4 is the version that
was accepted to NAACL2018 | null | null | A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference | ['Adina Williams', 'Nikita Nangia', 'Samuel R. Bowman'] | 2,017 | North American Chapter of the Association for Computational Linguistics | 4,507 | 56 | ['Computer Science'] |
1,704.07809 | Hand Keypoint Detection in Single Images using Multiview Bootstrapping | ['Tomas Simon', 'Hanbyul Joo', 'Iain Matthews', 'Yaser Sheikh'] | ['cs.CV'] | We present an approach that uses a multi-camera system to train fine-grained
detectors for keypoints that are prone to occlusion, such as the joints of a
hand. We call this procedure multiview bootstrapping: first, an initial
keypoint detector is used to produce noisy labels in multiple views of the
hand. The noisy det... | 2017-04-25T17:37:48Z | CVPR 2017 | null | null | Hand Keypoint Detection in Single Images Using Multiview Bootstrapping | ['T. Simon', 'H. Joo', 'I. Matthews', 'Yaser Sheikh'] | 2,017 | Computer Vision and Pattern Recognition | 1,116 | 32 | ['Computer Science'] |
1,705.00106 | Learning to Ask: Neural Question Generation for Reading Comprehension | ['Xinya Du', 'Junru Shao', 'Claire Cardie'] | ['cs.CL', 'cs.AI'] | We study automatic question generation for sentences from text passages in
reading comprehension. We introduce an attention-based sequence learning model
for the task and investigate the effect of encoding sentence- vs.
paragraph-level information. In contrast to all previous work, our model does
not rely on hand-craft... | 2017-04-29T01:08:48Z | Accepted to ACL 2017, 11 pages | null | null | Learning to Ask: Neural Question Generation for Reading Comprehension | ['Xinya Du', 'Junru Shao', 'Claire Cardie'] | 2,017 | Annual Meeting of the Association for Computational Linguistics | 664 | 42 | ['Computer Science'] |
1,705.00648 | "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News
Detection | ['William Yang Wang'] | ['cs.CL', 'cs.CY'] | Automatic fake news detection is a challenging problem in deception
detection, and it has tremendous real-world political and social impacts.
However, statistical approaches to combating fake news has been dramatically
limited by the lack of labeled benchmark datasets. In this paper, we present
liar: a new, publicly av... | 2017-05-01T18:20:47Z | ACL 2017 | null | null | “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection | ['William Yang Wang'] | 2,017 | Annual Meeting of the Association for Computational Linguistics | 1,372 | 16 | ['Computer Science'] |
1,705.00652 | Efficient Natural Language Response Suggestion for Smart Reply | ['Matthew Henderson', 'Rami Al-Rfou', 'Brian Strope', 'Yun-hsuan Sung', 'Laszlo Lukacs', 'Ruiqi Guo', 'Sanjiv Kumar', 'Balint Miklos', 'Ray Kurzweil'] | ['cs.CL'] | This paper presents a computationally efficient machine-learned method for
natural language response suggestion. Feed-forward neural networks using n-gram
embedding features encode messages into vectors which are optimized to give
message-response pairs a high dot-product value. An optimized search finds
response sugge... | 2017-05-01T18:24:15Z | null | null | null | null | null | null | null | null | null | null |
1,705.02315 | ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on
Weakly-Supervised Classification and Localization of Common Thorax Diseases | ['Xiaosong Wang', 'Yifan Peng', 'Le Lu', 'Zhiyong Lu', 'Mohammadhadi Bagheri', 'Ronald M. Summers'] | ['cs.CV', 'cs.CL'] | The chest X-ray is one of the most commonly accessible radiological
examinations for screening and diagnosis of many lung diseases. A tremendous
number of X-ray imaging studies accompanied by radiological reports are
accumulated and stored in many modern hospitals' Picture Archiving and
Communication Systems (PACS). On... | 2017-05-05T17:31:12Z | CVPR 2017 spotlight;V1: CVPR submission+supplementary; V2: Statistics
and benchmark results on published ChestX-ray14 dataset are updated in
Appendix B V3: Minor correction V4: new data download link upated:
https://nihcc.app.box.com/v/ChestXray-NIHCC V5: Update benchmark results on
the published data split in ... | IEEE CVPR 2017, pp. 2097-2106 (2017) | 10.1109/CVPR.2017.369 | null | null | null | null | null | null | null |
1,705.03551 | TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for
Reading Comprehension | ['Mandar Joshi', 'Eunsol Choi', 'Daniel S. Weld', 'Luke Zettlemoyer'] | ['cs.CL'] | We present TriviaQA, a challenging reading comprehension dataset containing
over 650K question-answer-evidence triples. TriviaQA includes 95K
question-answer pairs authored by trivia enthusiasts and independently gathered
evidence documents, six per question on average, that provide high quality
distant supervision for... | 2017-05-09T21:35:07Z | Added references, fixed typos, minor baseline update | null | null | TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension | ['Mandar Joshi', 'Eunsol Choi', 'Daniel S. Weld', 'Luke Zettlemoyer'] | 2,017 | Annual Meeting of the Association for Computational Linguistics | 2,696 | 38 | ['Computer Science'] |
1,705.0775 | Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset | ['Joao Carreira', 'Andrew Zisserman'] | ['cs.CV', 'cs.LG'] | The paucity of videos in current action classification datasets (UCF-101 and
HMDB-51) has made it difficult to identify good video architectures, as most
methods obtain similar performance on existing small-scale benchmarks. This
paper re-evaluates state-of-the-art architectures in light of the new Kinetics
Human Actio... | 2017-05-22T13:57:53Z | Removed references to mini-kinetics dataset that was never made
publicly available and repeated all experiments on the full Kinetics dataset | null | null | null | null | null | null | null | null | null |
1,705.11168 | Are distributional representations ready for the real world? Evaluating
word vectors for grounded perceptual meaning | ['Li Lucy', 'Jon Gauthier'] | ['cs.CL'] | Distributional word representation methods exploit word co-occurrences to
build compact vector encodings of words. While these representations enjoy
widespread use in modern natural language processing, it is unclear whether
they accurately encode all necessary facets of conceptual meaning. In this
paper, we evaluate h... | 2017-05-31T16:31:54Z | Accepted at RoboNLP 2017 | null | null | Are Distributional Representations Ready for the Real World? Evaluating Word Vectors for Grounded Perceptual Meaning | ['Li Lucy', 'Jon Gauthier'] | 2,017 | RoboNLP@ACL | 62 | 31 | ['Computer Science'] |
1,706.01905 | Parameter Space Noise for Exploration | ['Matthias Plappert', 'Rein Houthooft', 'Prafulla Dhariwal', 'Szymon Sidor', 'Richard Y. Chen', 'Xi Chen', 'Tamim Asfour', 'Pieter Abbeel', 'Marcin Andrychowicz'] | ['cs.LG', 'cs.AI', 'cs.NE', 'cs.RO', 'stat.ML'] | Deep reinforcement learning (RL) methods generally engage in exploratory
behavior through noise injection in the action space. An alternative is to add
noise directly to the agent's parameters, which can lead to more consistent
exploration and a richer set of behaviors. Methods such as evolutionary
strategies use param... | 2017-06-06T18:09:29Z | Updated to camera-ready ICLR submission | null | null | null | null | null | null | null | null | null |
1,706.02275 | Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments | ['Ryan Lowe', 'Yi Wu', 'Aviv Tamar', 'Jean Harb', 'Pieter Abbeel', 'Igor Mordatch'] | ['cs.LG', 'cs.AI', 'cs.NE'] | We explore deep reinforcement learning methods for multi-agent domains. We
begin by analyzing the difficulty of traditional algorithms in the multi-agent
case: Q-learning is challenged by an inherent non-stationarity of the
environment, while policy gradient suffers from a variance that increases as
the number of agent... | 2017-06-07T17:35:00Z | null | null | null | Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments | ['Ryan Lowe', 'Yi Wu', 'Aviv Tamar', 'J. Harb', 'P. Abbeel', 'Igor Mordatch'] | 2,017 | Neural Information Processing Systems | 4,543 | 40 | ['Computer Science', 'Mathematics'] |
1,706.02677 | Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour | ['Priya Goyal', 'Piotr Dollár', 'Ross Girshick', 'Pieter Noordhuis', 'Lukasz Wesolowski', 'Aapo Kyrola', 'Andrew Tulloch', 'Yangqing Jia', 'Kaiming He'] | ['cs.CV', 'cs.DC', 'cs.LG'] | Deep learning thrives with large neural networks and large datasets. However,
larger networks and larger datasets result in longer training times that impede
research and development progress. Distributed synchronous SGD offers a
potential solution to this problem by dividing SGD minibatches over a pool of
parallel wor... | 2017-06-08T16:51:53Z | Tech report (v2: correct typos) | null | null | Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour | ['Priya Goyal', 'Piotr Dollár', 'Ross B. Girshick', 'P. Noordhuis', 'Lukasz Wesolowski', 'Aapo Kyrola', 'Andrew Tulloch', 'Yangqing Jia', 'Kaiming He'] | 2,017 | arXiv.org | 3,693 | 43 | ['Computer Science'] |
1,706.03762 | Attention Is All You Need | ['Ashish Vaswani', 'Noam Shazeer', 'Niki Parmar', 'Jakob Uszkoreit', 'Llion Jones', 'Aidan N. Gomez', 'Lukasz Kaiser', 'Illia Polosukhin'] | ['cs.CL', 'cs.LG'] | The dominant sequence transduction models are based on complex recurrent or
convolutional neural networks in an encoder-decoder configuration. The best
performing models also connect the encoder and decoder through an attention
mechanism. We propose a new simple network architecture, the Transformer, based
solely on at... | 2017-06-12T17:57:34Z | 15 pages, 5 figures | null | null | Attention is All you Need | ['Ashish Vaswani', 'Noam M. Shazeer', 'Niki Parmar', 'Jakob Uszkoreit', 'Llion Jones', 'Aidan N. Gomez', 'Lukasz Kaiser', 'I. Polosukhin'] | 2,017 | Neural Information Processing Systems | 133,599 | 41 | ['Computer Science'] |
1,706.05565 | Towards Neural Phrase-based Machine Translation | ['Po-Sen Huang', 'Chong Wang', 'Sitao Huang', 'Dengyong Zhou', 'Li Deng'] | ['cs.CL', 'stat.ML'] | In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our
method explicitly models the phrase structures in output sequences using
Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence
modeling method. To mitigate the monotonic alignment requirement of SWAN, we
introduce a new ... | 2017-06-17T17:36:23Z | in International Conference on Learning Representations (ICLR) 2018 | null | null | Toward Neural Phrase-based Machine Translation | ['Po-Sen Huang', 'Chong Wang', 'Dengyong Zhou', 'L. Deng'] | 2,017 | null | 3 | 17 | ['Computer Science', 'Mathematics'] |
1,706.05587 | Rethinking Atrous Convolution for Semantic Image Segmentation | ['Liang-Chieh Chen', 'George Papandreou', 'Florian Schroff', 'Hartwig Adam'] | ['cs.CV'] | In this work, we revisit atrous convolution, a powerful tool to explicitly
adjust filter's field-of-view as well as control the resolution of feature
responses computed by Deep Convolutional Neural Networks, in the application of
semantic image segmentation. To handle the problem of segmenting objects at
multiple scale... | 2017-06-17T22:48:57Z | Add more experimental results | null | null | null | null | null | null | null | null | null |
1,706.085 | GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash
Equilibrium | ['Martin Heusel', 'Hubert Ramsauer', 'Thomas Unterthiner', 'Bernhard Nessler', 'Sepp Hochreiter'] | ['cs.LG', 'stat.ML'] | Generative Adversarial Networks (GANs) excel at creating realistic images
with complex models for which maximum likelihood is infeasible. However, the
convergence of GAN training has still not been proved. We propose a two
time-scale update rule (TTUR) for training GANs with stochastic gradient
descent on arbitrary GAN... | 2017-06-26T17:45:23Z | Implementations are available at: https://github.com/bioinf-jku/TTUR | Advances in Neural Information Processing Systems 30 (NIPS 2017) | null | GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium | ['M. Heusel', 'Hubert Ramsauer', 'Thomas Unterthiner', 'Bernhard Nessler', 'G. Klambauer', 'Sepp Hochreiter'] | 2,017 | arXiv.org | 466 | 66 | ['Computer Science', 'Mathematics'] |
1,706.09588 | Multi-scale Multi-band DenseNets for Audio Source Separation | ['Naoya Takahashi', 'Yuki Mitsufuji'] | ['cs.SD', 'cs.CL', 'cs.MM'] | This paper deals with the problem of audio source separation. To handle the
complex and ill-posed nature of the problems of audio source separation, the
current state-of-the-art approaches employ deep neural networks to obtain
instrumental spectra from a mixture. In this study, we propose a novel network
architecture t... | 2017-06-29T05:56:06Z | to appear at WASPAA 2017 | null | null | null | null | null | null | null | null | null |
1,707.01629 | Dual Path Networks | ['Yunpeng Chen', 'Jianan Li', 'Huaxin Xiao', 'Xiaojie Jin', 'Shuicheng Yan', 'Jiashi Feng'] | ['cs.CV'] | In this work, we present a simple, highly efficient and modularized Dual Path
Network (DPN) for image classification which presents a new topology of
connection paths internally. By revealing the equivalence of the
state-of-the-art Residual Network (ResNet) and Densely Convolutional Network
(DenseNet) within the HORNN ... | 2017-07-06T04:05:14Z | for code and models, see https://github.com/cypw/DPNs | null | null | null | null | null | null | null | null | null |
1,707.02921 | Enhanced Deep Residual Networks for Single Image Super-Resolution | ['Bee Lim', 'Sanghyun Son', 'Heewon Kim', 'Seungjun Nah', 'Kyoung Mu Lee'] | ['cs.CV'] | Recent research on super-resolution has progressed with the development of
deep convolutional neural networks (DCNN). In particular, residual learning
techniques exhibit improved performance. In this paper, we develop an enhanced
deep super-resolution network (EDSR) with performance exceeding those of
current state-of-... | 2017-07-10T16:07:30Z | To appear in CVPR 2017 workshop. Best paper award of the NTIRE2017
workshop, and the winners of the NTIRE2017 Challenge on Single Image
Super-Resolution | null | null | Enhanced Deep Residual Networks for Single Image Super-Resolution | ['Bee Lim', 'Sanghyun Son', 'Heewon Kim', 'Seungjun Nah', 'Kyoung Mu Lee'] | 2,017 | 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | 5,951 | 37 | ['Computer Science'] |
1,707.06347 | Proximal Policy Optimization Algorithms | ['John Schulman', 'Filip Wolski', 'Prafulla Dhariwal', 'Alec Radford', 'Oleg Klimov'] | ['cs.LG'] | We propose a new family of policy gradient methods for reinforcement
learning, which alternate between sampling data through interaction with the
environment, and optimizing a "surrogate" objective function using stochastic
gradient ascent. Whereas standard policy gradient methods perform one gradient
update per data s... | 2017-07-20T02:32:33Z | null | null | null | null | null | null | null | null | null | null |
1,707.06484 | Deep Layer Aggregation | ['Fisher Yu', 'Dequan Wang', 'Evan Shelhamer', 'Trevor Darrell'] | ['cs.CV', 'cs.LG'] | Visual recognition requires rich representations that span levels from low to
high, scales from small to large, and resolutions from fine to coarse. Even
with the depth of features in a convolutional network, a layer in isolation is
not enough: compounding and aggregating these representations improves
inference of wha... | 2017-07-20T12:59:08Z | Published at the Conference on Computer Vision and Pattern
Recognition (CVPR) 2018 | null | null | null | null | null | null | null | null | null |
1,707.07012 | Learning Transferable Architectures for Scalable Image Recognition | ['Barret Zoph', 'Vijay Vasudevan', 'Jonathon Shlens', 'Quoc V. Le'] | ['cs.CV', 'cs.LG', 'stat.ML'] | Developing neural network image classification models often requires
significant architecture engineering. In this paper, we study a method to learn
the model architectures directly on the dataset of interest. As this approach
is expensive when the dataset is large, we propose to search for an
architectural building bl... | 2017-07-21T18:10:26Z | null | null | null | Learning Transferable Architectures for Scalable Image Recognition | ['Barret Zoph', 'Vijay Vasudevan', 'Jonathon Shlens', 'Quoc V. Le'] | 2,017 | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition | 5,623 | 77 | ['Computer Science'] |
1,708.00055 | SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and
Cross-lingual Focused Evaluation | ['Daniel Cer', 'Mona Diab', 'Eneko Agirre', 'Iñigo Lopez-Gazpio', 'Lucia Specia'] | ['cs.CL', '68T50', 'I.2.7'] | Semantic Textual Similarity (STS) measures the meaning similarity of
sentences. Applications include machine translation (MT), summarization,
generation, question answering (QA), short answer grading, semantic search,
dialog and conversational systems. The STS shared task is a venue for assessing
the current state-of-t... | 2017-07-31T20:12:06Z | To appear in proceedings of the SemEval workshop at ACL 2017; 14
pages, 14 Tables, 1 Figure | null | 10.18653/v1/S17-2001 | SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation | ['Daniel Matthew Cer', 'Mona T. Diab', 'Eneko Agirre', 'I. Lopez-Gazpio', 'Lucia Specia'] | 2,017 | International Workshop on Semantic Evaluation | 1,894 | 92 | ['Computer Science'] |
1,708.00524 | Using millions of emoji occurrences to learn any-domain representations
for detecting sentiment, emotion and sarcasm | ['Bjarke Felbo', 'Alan Mislove', 'Anders Søgaard', 'Iyad Rahwan', 'Sune Lehmann'] | ['stat.ML', 'cs.LG'] | NLP tasks are often limited by scarcity of manually annotated data. In social
media sentiment analysis and related tasks, researchers have therefore used
binarized emoticons and specific hashtags as forms of distant supervision. Our
paper shows that by extending the distant supervision to a more diverse set of
noisy la... | 2017-08-01T21:28:42Z | Accepted at EMNLP 2017. Please include EMNLP in any citations. Minor
changes from the EMNLP camera-ready version. 9 pages + references and
supplementary material | Proceedings of the 2017 Conference on Empirical Methods in Natural
Language Processing | 10.18653/v1/D17-1169 | Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm | ['Bjarke Felbo', 'A. Mislove', 'Anders Søgaard', 'Iyad Rahwan', 'S. Lehmann'] | 2,017 | Conference on Empirical Methods in Natural Language Processing | 744 | 47 | ['Mathematics', 'Computer Science'] |
1,708.02002 | Focal Loss for Dense Object Detection | ['Tsung-Yi Lin', 'Priya Goyal', 'Ross Girshick', 'Kaiming He', 'Piotr Dollár'] | ['cs.CV'] | The highest accuracy object detectors to date are based on a two-stage
approach popularized by R-CNN, where a classifier is applied to a sparse set of
candidate object locations. In contrast, one-stage detectors that are applied
over a regular, dense sampling of possible object locations have the potential
to be faster... | 2017-08-07T06:32:42Z | null | null | null | null | null | null | null | null | null | null |
1,708.02182 | Regularizing and Optimizing LSTM Language Models | ['Stephen Merity', 'Nitish Shirish Keskar', 'Richard Socher'] | ['cs.CL', 'cs.LG', 'cs.NE'] | Recurrent neural networks (RNNs), such as long short-term memory networks
(LSTMs), serve as a fundamental building block for many sequence learning
tasks, including machine translation, language modeling, and question
answering. In this paper, we consider the specific problem of word-level
language modeling and investi... | 2017-08-07T16:03:44Z | null | null | null | Regularizing and Optimizing LSTM Language Models | ['Stephen Merity', 'N. Keskar', 'R. Socher'] | 2,017 | International Conference on Learning Representations | 1,099 | 47 | ['Computer Science'] |
1,708.02657 | Which Encoding is the Best for Text Classification in Chinese, English,
Japanese and Korean? | ['Xiang Zhang', 'Yann LeCun'] | ['cs.CL', 'cs.LG'] | This article offers an empirical study on the different ways of encoding
Chinese, Japanese, Korean (CJK) and English languages for text classification.
Different encoding levels are studied, including UTF-8 bytes, characters,
words, romanized characters and romanized words. For all encoding levels,
whenever applicable,... | 2017-08-08T21:24:44Z | null | null | null | null | null | null | null | null | null | null |
1,708.0712 | Super-Convergence: Very Fast Training of Neural Networks Using Large
Learning Rates | ['Leslie N. Smith', 'Nicholay Topin'] | ['cs.LG', 'cs.CV', 'cs.NE', 'stat.ML'] | In this paper, we describe a phenomenon, which we named "super-convergence",
where neural networks can be trained an order of magnitude faster than with
standard training methods. The existence of super-convergence is relevant to
understanding why deep networks generalize well. One of the key elements of
super-converge... | 2017-08-23T17:51:57Z | This paper was significantly revised to show super-convergence as a
general fast training methodology | null | null | Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates | ['L. Smith', 'Nicholay Topin'] | 2,017 | arXiv.org | 518 | 43 | ['Computer Science', 'Mathematics'] |
1,708.08197 | Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in
Unconstrained Environments | ['Tianyue Zheng', 'Weihong Deng', 'Jiani Hu'] | ['cs.CV', 'cs.DB'] | Labeled Faces in the Wild (LFW) database has been widely utilized as the
benchmark of unconstrained face verification and due to big data driven machine
learning methods, the performance on the database approaches nearly 100%.
However, we argue that this accuracy may be too optimistic because of some
limiting factors. ... | 2017-08-28T06:07:27Z | 10 pages, 9 figures | null | null | Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments | ['Tianyue Zheng', 'Weihong Deng', 'Jiani Hu'] | 2,017 | arXiv.org | 420 | 25 | ['Computer Science'] |
1,708.09492 | Automatically Generating Commit Messages from Diffs using Neural Machine
Translation | ['Siyuan Jiang', 'Ameer Armaly', 'Collin McMillan'] | ['cs.SE', 'cs.CL'] | Commit messages are a valuable resource in comprehension of software
evolution, since they provide a record of changes such as feature additions and
bug repairs. Unfortunately, programmers often neglect to write good commit
messages. Different techniques have been proposed to help programmers by
automatically writing t... | 2017-08-30T22:26:48Z | Preprint version. Accepted in ASE 2017, the 32nd IEEE/ACM
International Conference on Automated Software Engineering | null | null | Automatically generating commit messages from diffs using neural machine translation | ['Siyuan Jiang', 'A. Armaly', 'Collin McMillan'] | 2,017 | International Conference on Automated Software Engineering | 261 | 61 | ['Computer Science'] |
1,709.00029 | EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and
Land Cover Classification | ['Patrick Helber', 'Benjamin Bischke', 'Andreas Dengel', 'Damian Borth'] | ['cs.CV', 'cs.LG'] | In this paper, we address the challenge of land use and land cover
classification using Sentinel-2 satellite images. The Sentinel-2 satellite
images are openly and freely accessible provided in the Earth observation
program Copernicus. We present a novel dataset based on Sentinel-2 satellite
images covering 13 spectral... | 2017-08-31T18:19:10Z | null | null | null | null | null | null | null | null | null | null |
1,709.00103 | Seq2SQL: Generating Structured Queries from Natural Language using
Reinforcement Learning | ['Victor Zhong', 'Caiming Xiong', 'Richard Socher'] | ['cs.CL', 'cs.AI'] | A significant amount of the world's knowledge is stored in relational
databases. However, the ability for users to retrieve facts from a database is
limited due to a lack of understanding of query languages such as SQL. We
propose Seq2SQL, a deep neural network for translating natural language
questions to correspondin... | 2017-08-31T23:12:15Z | 12 pages, 5 figures | null | null | Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning | ['Victor Zhong', 'Caiming Xiong', 'R. Socher'] | 2,017 | arXiv.org | 1,211 | 44 | ['Computer Science'] |
1,709.01507 | Squeeze-and-Excitation Networks | ['Jie Hu', 'Li Shen', 'Samuel Albanie', 'Gang Sun', 'Enhua Wu'] | ['cs.CV'] | The central building block of convolutional neural networks (CNNs) is the
convolution operator, which enables networks to construct informative features
by fusing both spatial and channel-wise information within local receptive
fields at each layer. A broad range of prior research has investigated the
spatial component... | 2017-09-05T17:42:13Z | journal version of the CVPR 2018 paper, accepted by TPAMI | null | null | Squeeze-and-Excitation Networks | ['Jie Hu', 'Li Shen', 'Samuel Albanie', 'Gang Sun', 'E. Wu'] | 2,017 | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition | 26,779 | 85 | ['Computer Science'] |
1,709.02984 | Sentiment Polarity Detection for Software Development | ['Fabio Calefato', 'Filippo Lanubile', 'Federico Maiorano', 'Nicole Novielli'] | ['cs.SE', 'cs.CL'] | The role of sentiment analysis is increasingly emerging to study software
developers' emotions by mining crowd-generated content within social software
engineering tools. However, off-the-shelf sentiment analysis tools have been
trained on non-technical domains and general-purpose social media, thus
resulting in miscla... | 2017-09-09T17:28:10Z | Cite as: Calefato, F., Lanubile, F., Maiorano, F., Novielli N. Empir
Software Eng (2017). https://doi.org/10.1007/s10664-017-9546-9 Full-text
view-only version here: http://rdcu.be/vZrG, Empir Software Eng (2017) | Empirical Software Engineering, June 2018, Volume 23, Issue 3, pp
1352 - 1382 | 10.1007/s10664-017-9546-9 | null | null | null | null | null | null | null |
1,709.08267 | HDLTex: Hierarchical Deep Learning for Text Classification | ['Kamran Kowsari', 'Donald E. Brown', 'Mojtaba Heidarysafa', 'Kiana Jafari Meimandi', 'Matthew S. Gerber', 'Laura E. Barnes'] | ['cs.LG', 'cs.AI', 'cs.CL', 'cs.CV', 'cs.IR'] | The continually increasing number of documents produced each year
necessitates ever improving information processing methods for searching,
retrieving, and organizing text. Central to these information processing
methods is document classification, which has become an important application
for supervised learning. Rece... | 2017-09-24T21:58:12Z | ICMLA 2017 | null | 10.1109/ICMLA.2017.0-134 | null | null | null | null | null | null | null |
1,710.0374 | Mixed Precision Training | ['Paulius Micikevicius', 'Sharan Narang', 'Jonah Alben', 'Gregory Diamos', 'Erich Elsen', 'David Garcia', 'Boris Ginsburg', 'Michael Houston', 'Oleksii Kuchaiev', 'Ganesh Venkatesh', 'Hao Wu'] | ['cs.AI', 'cs.LG', 'stat.ML'] | Deep neural networks have enabled progress in a wide variety of applications.
Growing the size of the neural network typically results in improved accuracy.
As model sizes grow, the memory and compute requirements for training these
models also increases. We introduce a technique to train deep neural networks
using hal... | 2017-10-10T17:42:04Z | Published as a conference paper at ICLR 2018 | null | null | null | null | null | null | null | null | null |
1,710.06071 | PubMed 200k RCT: a Dataset for Sequential Sentence Classification in
Medical Abstracts | ['Franck Dernoncourt', 'Ji Young Lee'] | ['cs.CL', 'cs.AI', 'stat.ML'] | We present PubMed 200k RCT, a new dataset based on PubMed for sequential
sentence classification. The dataset consists of approximately 200,000
abstracts of randomized controlled trials, totaling 2.3 million sentences. Each
sentence of each abstract is labeled with their role in the abstract using one
of the following ... | 2017-10-17T03:22:00Z | Accepted as a conference paper at IJCNLP 2017 | null | null | null | null | null | null | null | null | null |
1,710.08969 | Efficiently Trainable Text-to-Speech System Based on Deep Convolutional
Networks with Guided Attention | ['Hideyuki Tachibana', 'Katsuya Uenoyama', 'Shunsuke Aihara'] | ['cs.SD', 'cs.AI', 'cs.LG', 'eess.AS'] | This paper describes a novel text-to-speech (TTS) technique based on deep
convolutional neural networks (CNN), without use of any recurrent units.
Recurrent neural networks (RNN) have become a standard technique to model
sequential data recently, and this technique has been used in some cutting-edge
neural TTS techniqu... | 2017-10-24T19:56:32Z | 5 pages, 3figures, IEEE ICASSP 2018 | Proc. ICASSP (2018) 4784-4788 | 10.1109/ICASSP.2018.8461829 | null | null | null | null | null | null | null |
1,710.09412 | mixup: Beyond Empirical Risk Minimization | ['Hongyi Zhang', 'Moustapha Cisse', 'Yann N. Dauphin', 'David Lopez-Paz'] | ['cs.LG', 'stat.ML'] | Large deep neural networks are powerful, but exhibit undesirable behaviors
such as memorization and sensitivity to adversarial examples. In this work, we
propose mixup, a simple learning principle to alleviate these issues. In
essence, mixup trains a neural network on convex combinations of pairs of
examples and their ... | 2017-10-25T18:30:49Z | ICLR camera ready version. Changes vs V1: fix repo URL; add ablation
studies; add mixup + dropout etc | null | null | mixup: Beyond Empirical Risk Minimization | ['Hongyi Zhang', 'Moustapha Cissé', 'Yann Dauphin', 'David Lopez-Paz'] | 2,017 | International Conference on Learning Representations | 9,848 | 39 | ['Mathematics', 'Computer Science'] |
1,710.10196 | Progressive Growing of GANs for Improved Quality, Stability, and
Variation | ['Tero Karras', 'Timo Aila', 'Samuli Laine', 'Jaakko Lehtinen'] | ['cs.NE', 'cs.LG', 'stat.ML'] | We describe a new training methodology for generative adversarial networks.
The key idea is to grow both the generator and discriminator progressively:
starting from a low resolution, we add new layers that model increasingly fine
details as training progresses. This both speeds the training up and greatly
stabilizes i... | 2017-10-27T15:28:35Z | Final ICLR 2018 version | null | null | null | null | null | null | null | null | null |
1,710.10467 | Generalized End-to-End Loss for Speaker Verification | ['Li Wan', 'Quan Wang', 'Alan Papir', 'Ignacio Lopez Moreno'] | ['eess.AS', 'cs.CL', 'cs.LG', 'stat.ML'] | In this paper, we propose a new loss function called generalized end-to-end
(GE2E) loss, which makes the training of speaker verification models more
efficient than our previous tuple-based end-to-end (TE2E) loss function. Unlike
TE2E, the GE2E loss function updates the network in a way that emphasizes
examples that ar... | 2017-10-28T13:51:51Z | Published at ICASSP 2018 | null | null | Generalized End-to-End Loss for Speaker Verification | ['Li Wan', 'Quan Wang', 'Alan Papir', 'I. López-Moreno'] | 2,017 | IEEE International Conference on Acoustics, Speech, and Signal Processing | 933 | 18 | ['Computer Science', 'Engineering', 'Mathematics'] |
1,710.10639 | JESC: Japanese-English Subtitle Corpus | ['Reid Pryzant', 'Yongjoo Chung', 'Dan Jurafsky', 'Denny Britz'] | ['cs.CL'] | In this paper we describe the Japanese-English Subtitle Corpus (JESC). JESC
is a large Japanese-English parallel corpus covering the underrepresented
domain of conversational dialogue. It consists of more than 3.2 million
examples, making it the largest freely available dataset of its kind. The
corpus was assembled by ... | 2017-10-29T16:15:30Z | To appear at LREC 2018. Project website updated | null | null | JESC: Japanese-English Subtitle Corpus | ['Reid Pryzant', 'Young-joo Chung', 'Dan Jurafsky', 'D. Britz'] | 2,017 | International Conference on Language Resources and Evaluation | 70 | 28 | ['Computer Science'] |
1,710.10903 | Graph Attention Networks | ['Petar Veličković', 'Guillem Cucurull', 'Arantxa Casanova', 'Adriana Romero', 'Pietro Liò', 'Yoshua Bengio'] | ['stat.ML', 'cs.AI', 'cs.LG', 'cs.SI'] | We present graph attention networks (GATs), novel neural network
architectures that operate on graph-structured data, leveraging masked
self-attentional layers to address the shortcomings of prior methods based on
graph convolutions or their approximations. By stacking layers in which nodes
are able to attend over thei... | 2017-10-30T12:41:12Z | To appear at ICLR 2018. 12 pages, 2 figures | null | null | null | null | null | null | null | null | null |
1,711.00043 | Unsupervised Machine Translation Using Monolingual Corpora Only | ['Guillaume Lample', 'Alexis Conneau', 'Ludovic Denoyer', "Marc'Aurelio Ranzato"] | ['cs.CL', 'cs.AI'] | Machine translation has recently achieved impressive performance thanks to
recent advances in deep learning and the availability of large-scale parallel
corpora. There have been numerous attempts to extend these successes to
low-resource language pairs, yet requiring tens of thousands of parallel
sentences. In this wor... | 2017-10-31T18:31:11Z | ICLR 2018 | null | null | Unsupervised Machine Translation Using Monolingual Corpora Only | ['Guillaume Lample', 'Ludovic Denoyer', "Marc'Aurelio Ranzato"] | 2,017 | International Conference on Learning Representations | 1,098 | 43 | ['Computer Science'] |
1,711.00199 | PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in
Cluttered Scenes | ['Yu Xiang', 'Tanner Schmidt', 'Venkatraman Narayanan', 'Dieter Fox'] | ['cs.CV', 'cs.RO'] | Estimating the 6D pose of known objects is important for robots to interact
with the real world. The problem is challenging due to the variety of objects
as well as the complexity of a scene caused by clutter and occlusions between
objects. In this work, we introduce PoseCNN, a new Convolutional Neural Network
for 6D o... | 2017-11-01T04:10:58Z | Accepted to RSS 2018 | null | null | null | null | null | null | null | null | null |
1,711.00937 | Neural Discrete Representation Learning | ['Aaron van den Oord', 'Oriol Vinyals', 'Koray Kavukcuoglu'] | ['cs.LG'] | Learning useful representations without supervision remains a key challenge
in machine learning. In this paper, we propose a simple yet powerful generative
model that learns such discrete representations. Our model, the Vector
Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways:
the encoder ne... | 2017-11-02T21:14:44Z | null | null | null | null | null | null | null | null | null | null |
1,711.05101 | Decoupled Weight Decay Regularization | ['Ilya Loshchilov', 'Frank Hutter'] | ['cs.LG', 'cs.NE', 'math.OC'] | L$_2$ regularization and weight decay regularization are equivalent for
standard stochastic gradient descent (when rescaled by the learning rate), but
as we demonstrate this is \emph{not} the case for adaptive gradient algorithms,
such as Adam. While common implementations of these algorithms employ L$_2$
regularizatio... | 2017-11-14T14:24:06Z | Published as a conference paper at ICLR 2019 | null | null | null | null | null | null | null | null | null |
1,711.05732 | ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with
Millions of Machine Translations | ['John Wieting', 'Kevin Gimpel'] | ['cs.CL'] | We describe PARANMT-50M, a dataset of more than 50 million English-English
sentential paraphrase pairs. We generated the pairs automatically by using
neural machine translation to translate the non-English side of a large
parallel corpus, following Wieting et al. (2017). Our hope is that ParaNMT-50M
can be a valuable r... | 2017-11-15T18:59:29Z | null | null | null | null | null | null | null | null | null | null |
1,711.07128 | Hello Edge: Keyword Spotting on Microcontrollers | ['Yundong Zhang', 'Naveen Suda', 'Liangzhen Lai', 'Vikas Chandra'] | ['cs.SD', 'cs.CL', 'cs.LG', 'cs.NE', 'eess.AS'] | Keyword spotting (KWS) is a critical component for enabling speech based user
interactions on smart devices. It requires real-time response and high accuracy
for good user experience. Recently, neural networks have become an attractive
choice for KWS architecture because of their superior accuracy compared to
tradition... | 2017-11-20T03:19:03Z | Code available in github at
https://github.com/ARM-software/ML-KWS-for-MCU | null | null | Hello Edge: Keyword Spotting on Microcontrollers | ['Yundong Zhang', 'Naveen Suda', 'Liangzhen Lai', 'V. Chandra'] | 2,017 | arXiv.org | 438 | 36 | ['Computer Science', 'Engineering'] |
1,711.10337 | Are GANs Created Equal? A Large-Scale Study | ['Mario Lucic', 'Karol Kurach', 'Marcin Michalski', 'Sylvain Gelly', 'Olivier Bousquet'] | ['stat.ML', 'cs.LG'] | Generative adversarial networks (GAN) are a powerful subclass of generative
models. Despite a very rich research activity leading to numerous interesting
GAN algorithms, it is still very hard to assess which algorithm(s) perform
better than others. We conduct a neutral, multi-faceted large-scale empirical
study on stat... | 2017-11-28T15:19:53Z | NIPS'18: Added a section on the limitations of the study and
additional empirical results | null | null | Are GANs Created Equal? A Large-Scale Study | ['Mario Lucic', 'Karol Kurach', 'Marcin Michalski', 'S. Gelly', 'O. Bousquet'] | 2,017 | Neural Information Processing Systems | 1,015 | 28 | ['Mathematics', 'Computer Science'] |
1,711.11248 | A Closer Look at Spatiotemporal Convolutions for Action Recognition | ['Du Tran', 'Heng Wang', 'Lorenzo Torresani', 'Jamie Ray', 'Yann LeCun', 'Manohar Paluri'] | ['cs.CV'] | In this paper we discuss several forms of spatiotemporal convolutions for
video analysis and study their effects on action recognition. Our motivation
stems from the observation that 2D CNNs applied to individual frames of the
video have remained solid performers in action recognition. In this work we
empirically demon... | 2017-11-30T06:28:20Z | null | null | null | A Closer Look at Spatiotemporal Convolutions for Action Recognition | ['Du Tran', 'Heng Wang', 'L. Torresani', 'Jamie Ray', 'Yann LeCun', 'Manohar Paluri'] | 2,017 | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition | 3,047 | 45 | ['Computer Science'] |
1,712.00559 | Progressive Neural Architecture Search | ['Chenxi Liu', 'Barret Zoph', 'Maxim Neumann', 'Jonathon Shlens', 'Wei Hua', 'Li-Jia Li', 'Li Fei-Fei', 'Alan Yuille', 'Jonathan Huang', 'Kevin Murphy'] | ['cs.CV', 'cs.LG', 'stat.ML'] | We propose a new method for learning the structure of convolutional neural
networks (CNNs) that is more efficient than recent state-of-the-art methods
based on reinforcement learning and evolutionary algorithms. Our approach uses
a sequential model-based optimization (SMBO) strategy, in which we search for
structures i... | 2017-12-02T06:23:16Z | To appear in ECCV 2018 as oral. The code and checkpoint for PNASNet-5
trained on ImageNet (both Mobile and Large) can now be downloaded from
https://github.com/tensorflow/models/tree/master/research/slim#Pretrained.
Also see https://github.com/chenxi116/PNASNet.TF for refactored and
simplified TensorFlow code; ... | null | null | null | null | null | null | null | null | null |
1,712.00726 | Cascade R-CNN: Delving into High Quality Object Detection | ['Zhaowei Cai', 'Nuno Vasconcelos'] | ['cs.CV'] | In object detection, an intersection over union (IoU) threshold is required
to define positives and negatives. An object detector, trained with low IoU
threshold, e.g. 0.5, usually produces noisy detections. However, detection
performance tends to degrade with increasing the IoU thresholds. Two main
factors are respons... | 2017-12-03T07:24:45Z | null | null | null | null | null | null | null | null | null | null |
1,712.01815 | Mastering Chess and Shogi by Self-Play with a General Reinforcement
Learning Algorithm | ['David Silver', 'Thomas Hubert', 'Julian Schrittwieser', 'Ioannis Antonoglou', 'Matthew Lai', 'Arthur Guez', 'Marc Lanctot', 'Laurent Sifre', 'Dharshan Kumaran', 'Thore Graepel', 'Timothy Lillicrap', 'Karen Simonyan', 'Demis Hassabis'] | ['cs.AI', 'cs.LG'] | The game of chess is the most widely-studied domain in the history of
artificial intelligence. The strongest programs are based on a combination of
sophisticated search techniques, domain-specific adaptations, and handcrafted
evaluation functions that have been refined by human experts over several
decades. In contrast... | 2017-12-05T18:45:38Z | null | null | null | null | null | null | null | null | null | null |
1,712.05884 | Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram
Predictions | ['Jonathan Shen', 'Ruoming Pang', 'Ron J. Weiss', 'Mike Schuster', 'Navdeep Jaitly', 'Zongheng Yang', 'Zhifeng Chen', 'Yu Zhang', 'Yuxuan Wang', 'RJ Skerry-Ryan', 'Rif A. Saurous', 'Yannis Agiomyrgiannakis', 'Yonghui Wu'] | ['cs.CL'] | This paper describes Tacotron 2, a neural network architecture for speech
synthesis directly from text. The system is composed of a recurrent
sequence-to-sequence feature prediction network that maps character embeddings
to mel-scale spectrograms, followed by a modified WaveNet model acting as a
vocoder to synthesize t... | 2017-12-16T00:51:40Z | Accepted to ICASSP 2018 | null | null | null | null | null | null | null | null | null |
1,712.05889 | Ray: A Distributed Framework for Emerging AI Applications | ['Philipp Moritz', 'Robert Nishihara', 'Stephanie Wang', 'Alexey Tumanov', 'Richard Liaw', 'Eric Liang', 'Melih Elibol', 'Zongheng Yang', 'William Paul', 'Michael I. Jordan', 'Ion Stoica'] | ['cs.DC', 'cs.AI', 'cs.LG', 'stat.ML'] | The next generation of AI applications will continuously interact with the
environment and learn from these interactions. These applications impose new
and demanding systems requirements, both in terms of performance and
flexibility. In this paper, we consider these requirements and present Ray---a
distributed system t... | 2017-12-16T01:29:49Z | 17 pages, 14 figures, 13th USENIX Symposium on Operating Systems
Design and Implementation, 2018 | null | null | null | null | null | null | null | null | null |
1,712.06567 | Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative
for Training Deep Neural Networks for Reinforcement Learning | ['Felipe Petroski Such', 'Vashisht Madhavan', 'Edoardo Conti', 'Joel Lehman', 'Kenneth O. Stanley', 'Jeff Clune'] | ['cs.NE', 'cs.LG'] | Deep artificial neural networks (DNNs) are typically trained via
gradient-based learning algorithms, namely backpropagation. Evolution
strategies (ES) can rival backprop-based algorithms such as Q-learning and
policy gradients on challenging deep reinforcement learning (RL) problems.
However, ES can be considered a gra... | 2017-12-18T18:22:05Z | null | null | null | null | null | null | null | null | null | null |
1,712.0704 | The NarrativeQA Reading Comprehension Challenge | ['Tomáš Kočiský', 'Jonathan Schwarz', 'Phil Blunsom', 'Chris Dyer', 'Karl Moritz Hermann', 'Gábor Melis', 'Edward Grefenstette'] | ['cs.CL', 'cs.AI', 'cs.NE'] | Reading comprehension (RC)---in contrast to information retrieval---requires
integrating information and reasoning about events, entities, and their
relations across a full document. Question answering is conventionally used to
assess RC ability, in both artificial agents and children learning to read.
However, existin... | 2017-12-19T16:48:05Z | null | null | null | null | null | null | null | null | null | null |
1,712.07628 | Improving Generalization Performance by Switching from Adam to SGD | ['Nitish Shirish Keskar', 'Richard Socher'] | ['cs.LG', 'math.OC'] | Despite superior training outcomes, adaptive optimization methods such as
Adam, Adagrad or RMSprop have been found to generalize poorly compared to
Stochastic gradient descent (SGD). These methods tend to perform well in the
initial portion of training but are outperformed by SGD at later stages of
training. We investi... | 2017-12-20T18:34:08Z | null | null | null | Improving Generalization Performance by Switching from Adam to SGD | ['N. Keskar', 'R. Socher'] | 2,017 | arXiv.org | 524 | 31 | ['Computer Science', 'Mathematics'] |
1,712.07629 | SuperPoint: Self-Supervised Interest Point Detection and Description | ['Daniel DeTone', 'Tomasz Malisiewicz', 'Andrew Rabinovich'] | ['cs.CV'] | This paper presents a self-supervised framework for training interest point
detectors and descriptors suitable for a large number of multiple-view geometry
problems in computer vision. As opposed to patch-based neural networks, our
fully-convolutional model operates on full-sized images and jointly computes
pixel-level... | 2017-12-20T18:38:35Z | Camera-ready version for CVPR 2018 Deep Learning for Visual SLAM
Workshop (DL4VSLAM2018) | null | null | SuperPoint: Self-Supervised Interest Point Detection and Description | ['Daniel DeTone', 'Tomasz Malisiewicz', 'Andrew Rabinovich'] | 2,017 | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | 2,504 | 34 | ['Computer Science'] |
1,801.0069 | DeepMind Control Suite | ['Yuval Tassa', 'Yotam Doron', 'Alistair Muldal', 'Tom Erez', 'Yazhe Li', 'Diego de Las Casas', 'David Budden', 'Abbas Abdolmaleki', 'Josh Merel', 'Andrew Lefrancq', 'Timothy Lillicrap', 'Martin Riedmiller'] | ['cs.AI'] | The DeepMind Control Suite is a set of continuous control tasks with a
standardised structure and interpretable rewards, intended to serve as
performance benchmarks for reinforcement learning agents. The tasks are written
in Python and powered by the MuJoCo physics engine, making them easy to use and
modify. We include... | 2018-01-02T15:48:14Z | 24 pages, 7 figures, 2 tables | null | null | DeepMind Control Suite | ['Yuval Tassa', 'Yotam Doron', 'Alistair Muldal', 'Tom Erez', 'Yazhe Li', 'Diego de Las Casas', 'D. Budden', 'A. Abdolmaleki', 'J. Merel', 'Andrew Lefrancq', 'T. Lillicrap', 'Martin A. Riedmiller'] | 2,018 | arXiv.org | 1,144 | 37 | ['Computer Science'] |
1,801.01681 | VulDeePecker: A Deep Learning-Based System for Vulnerability Detection | ['Zhen Li', 'Deqing Zou', 'Shouhuai Xu', 'Xinyu Ou', 'Hai Jin', 'Sujuan Wang', 'Zhijun Deng', 'Yuyi Zhong'] | ['cs.CR', 'cs.AI', 'cs.LG'] | The automatic detection of software vulnerabilities is an important research
problem. However, existing solutions to this problem rely on human experts to
define features and often miss many vulnerabilities (i.e., incurring high false
negative rate). In this paper, we initiate the study of using deep
learning-based vul... | 2018-01-05T09:37:18Z | null | null | 10.14722/ndss.2018.23158 | null | null | null | null | null | null | null |
1,801.03924 | The Unreasonable Effectiveness of Deep Features as a Perceptual Metric | ['Richard Zhang', 'Phillip Isola', 'Alexei A. Efros', 'Eli Shechtman', 'Oliver Wang'] | ['cs.CV', 'cs.GR'] | While it is nearly effortless for humans to quickly assess the perceptual
similarity between two images, the underlying processes are thought to be quite
complex. Despite this, the most widely used perceptual metrics today, such as
PSNR and SSIM, are simple, shallow functions, and fail to account for many
nuances of hu... | 2018-01-11T18:54:17Z | Accepted to CVPR 2018; Code and data available at
https://www.github.com/richzhang/PerceptualSimilarity | null | null | The Unreasonable Effectiveness of Deep Features as a Perceptual Metric | ['Richard Zhang', 'Phillip Isola', 'Alexei A. Efros', 'Eli Shechtman', 'Oliver Wang'] | 2,018 | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition | 11,997 | 71 | ['Computer Science'] |
1,801.04381 | MobileNetV2: Inverted Residuals and Linear Bottlenecks | ['Mark Sandler', 'Andrew Howard', 'Menglong Zhu', 'Andrey Zhmoginov', 'Liang-Chieh Chen'] | ['cs.CV'] | In this paper we describe a new mobile architecture, MobileNetV2, that
improves the state of the art performance of mobile models on multiple tasks
and benchmarks as well as across a spectrum of different model sizes. We also
describe efficient ways of applying these mobile models to object detection in
a novel framewo... | 2018-01-13T04:46:26Z | null | The IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), 2018, pp. 4510-4520 | null | null | null | null | null | null | null | null |
1,801.06146 | Universal Language Model Fine-tuning for Text Classification | ['Jeremy Howard', 'Sebastian Ruder'] | ['cs.CL', 'cs.LG', 'stat.ML'] | Inductive transfer learning has greatly impacted computer vision, but
existing approaches in NLP still require task-specific modifications and
training from scratch. We propose Universal Language Model Fine-tuning
(ULMFiT), an effective transfer learning method that can be applied to any task
in NLP, and introduce tech... | 2018-01-18T17:54:52Z | ACL 2018, fixed denominator in Equation 3, line 3 | null | null | Fine-tuned Language Models for Text Classification | ['Jeremy Howard', 'Sebastian Ruder'] | 2,018 | arXiv.org | 276 | 67 | ['Computer Science', 'Mathematics'] |
1,801.07243 | Personalizing Dialogue Agents: I have a dog, do you have pets too? | ['Saizheng Zhang', 'Emily Dinan', 'Jack Urbanek', 'Arthur Szlam', 'Douwe Kiela', 'Jason Weston'] | ['cs.AI', 'cs.CL'] | Chit-chat models are known to have several problems: they lack specificity,
do not display a consistent personality and are often not very captivating. In
this work we present the task of making chit-chat more engaging by conditioning
on profile information. We collect data and train models to (i) condition on
their gi... | 2018-01-22T18:58:18Z | null | null | null | null | null | null | null | null | null | null |
1,801.07698 | ArcFace: Additive Angular Margin Loss for Deep Face Recognition | ['Jiankang Deng', 'Jia Guo', 'Jing Yang', 'Niannan Xue', 'Irene Kotsia', 'Stefanos Zafeiriou'] | ['cs.CV'] | Recently, a popular line of research in face recognition is adopting margins
in the well-established softmax loss function to maximize class separability.
In this paper, we first introduce an Additive Angular Margin Loss (ArcFace),
which not only has a clear geometric interpretation but also significantly
enhances the ... | 2018-01-23T18:39:19Z | ArcFace TPAMI version | null | 10.1109/TPAMI.2021.3087709 | ArcFace: Additive Angular Margin Loss for Deep Face Recognition | ['Jiankang Deng', 'J. Guo', 'J. Yang', 'Niannan Xue', 'I. Kotsia', 'S. Zafeiriou'] | 2,018 | IEEE Transactions on Pattern Analysis and Machine Intelligence | 220 | 121 | ['Computer Science', 'Medicine'] |
1,801.08092 | Generalizable Data-free Objective for Crafting Universal Adversarial
Perturbations | ['Konda Reddy Mopuri', 'Aditya Ganeshan', 'R. Venkatesh Babu'] | ['cs.CV', 'cs.AI', 'cs.LG'] | Machine learning models are susceptible to adversarial perturbations: small
changes to input that can cause large changes in output. It is also
demonstrated that there exist input-agnostic perturbations, called universal
adversarial perturbations, which can change the inference of target model on
most of the data sampl... | 2018-01-24T17:36:57Z | TPAMI | Repository: https://github.com/val-iisc/GD-UAP | null | null | null | null | null | null | null | null | null |
1,801.09847 | Open3D: A Modern Library for 3D Data Processing | ['Qian-Yi Zhou', 'Jaesik Park', 'Vladlen Koltun'] | ['cs.CV', 'cs.GR', 'cs.RO'] | Open3D is an open-source library that supports rapid development of software
that deals with 3D data. The Open3D frontend exposes a set of carefully
selected data structures and algorithms in both C++ and Python. The backend is
highly optimized and is set up for parallelization. Open3D was developed from a
clean slate ... | 2018-01-30T04:33:20Z | http://www.open3d.org | null | null | null | null | null | null | null | null | null |
1,802.02611 | Encoder-Decoder with Atrous Separable Convolution for Semantic Image
Segmentation | ['Liang-Chieh Chen', 'Yukun Zhu', 'George Papandreou', 'Florian Schroff', 'Hartwig Adam'] | ['cs.CV'] | Spatial pyramid pooling module or encode-decoder structure are used in deep
neural networks for semantic segmentation task. The former networks are able to
encode multi-scale contextual information by probing the incoming features with
filters or pooling operations at multiple rates and multiple effective
fields-of-vie... | 2018-02-07T19:37:11Z | ECCV 2018 camera ready | null | null | Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation | ['Liang-Chieh Chen', 'Yukun Zhu', 'G. Papandreou', 'Florian Schroff', 'Hartwig Adam'] | 2,018 | European Conference on Computer Vision | 13,279 | 88 | ['Computer Science'] |
1,802.0375 | FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy | ['Zheng Qin', 'Zhaoning Zhang', 'Xiaotao Chen', 'Yuxing Peng'] | ['cs.CV'] | We present Fast-Downsampling MobileNet (FD-MobileNet), an efficient and
accurate network for very limited computational budgets (e.g., 10-140 MFLOPs).
Our key idea is applying an aggressive downsampling strategy to MobileNet
framework. In FD-MobileNet, we perform 32$\times$ downsampling within 12
layers, only half the ... | 2018-02-11T15:01:47Z | 5 pages, 1 figures | null | null | null | null | null | null | null | null | null |
1,802.05957 | Spectral Normalization for Generative Adversarial Networks | ['Takeru Miyato', 'Toshiki Kataoka', 'Masanori Koyama', 'Yuichi Yoshida'] | ['cs.LG', 'cs.CV', 'stat.ML'] | One of the challenges in the study of generative adversarial networks is the
instability of its training. In this paper, we propose a novel weight
normalization technique called spectral normalization to stabilize the training
of the discriminator. Our new normalization technique is computationally light
and easy to in... | 2018-02-16T14:41:39Z | Published as a conference paper at ICLR 2018 | null | null | null | null | null | null | null | null | null |
1,802.06182 | CREPE: A Convolutional Representation for Pitch Estimation | ['Jong Wook Kim', 'Justin Salamon', 'Peter Li', 'Juan Pablo Bello'] | ['eess.AS', 'cs.LG', 'cs.SD', 'stat.ML'] | The task of estimating the fundamental frequency of a monophonic sound
recording, also known as pitch tracking, is fundamental to audio processing
with multiple applications in speech processing and music information
retrieval. To date, the best performing techniques, such as the pYIN algorithm,
are based on a combinat... | 2018-02-17T03:50:11Z | ICASSP 2018 | null | null | null | null | null | null | null | null | null |
1,802.06893 | Learning Word Vectors for 157 Languages | ['Edouard Grave', 'Piotr Bojanowski', 'Prakhar Gupta', 'Armand Joulin', 'Tomas Mikolov'] | ['cs.CL', 'cs.LG'] | Distributed word representations, or word vectors, have recently been applied
to many tasks in natural language processing, leading to state-of-the-art
performance. A key ingredient to the successful application of these
representations is to train them on very large corpora, and use these
pre-trained models in downstr... | 2018-02-19T22:32:47Z | Accepted to LREC | null | null | Learning Word Vectors for 157 Languages | ['Edouard Grave', 'Piotr Bojanowski', 'Prakhar Gupta', 'Armand Joulin', 'Tomas Mikolov'] | 2,018 | International Conference on Language Resources and Evaluation | 1,429 | 26 | ['Computer Science'] |
1,802.08435 | Efficient Neural Audio Synthesis | ['Nal Kalchbrenner', 'Erich Elsen', 'Karen Simonyan', 'Seb Noury', 'Norman Casagrande', 'Edward Lockhart', 'Florian Stimberg', 'Aaron van den Oord', 'Sander Dieleman', 'Koray Kavukcuoglu'] | ['cs.SD', 'cs.LG', 'eess.AS'] | Sequential models achieve state-of-the-art results in audio, visual and
textual domains with respect to both estimating the data distribution and
generating high-quality samples. Efficient sampling for this class of models
has however remained an elusive problem. With a focus on text-to-speech
synthesis, we describe a ... | 2018-02-23T08:20:23Z | 10 pages | null | null | Efficient Neural Audio Synthesis | ['Nal Kalchbrenner', 'Erich Elsen', 'K. Simonyan', 'Seb Noury', 'Norman Casagrande', 'Edward Lockhart', 'Florian Stimberg', 'Aäron van den Oord', 'S. Dieleman', 'K. Kavukcuoglu'] | 2,018 | International Conference on Machine Learning | 872 | 25 | ['Computer Science', 'Engineering'] |
1,802.0913 | Did You Really Just Have a Heart Attack? Towards Robust Detection of
Personal Health Mentions in Social Media | ['Payam Karisani', 'Eugene Agichtein'] | ['cs.CL'] | Millions of users share their experiences on social media sites, such as
Twitter, which in turn generate valuable data for public health monitoring,
digital epidemiology, and other analyses of population health at global scale.
The first, critical, task for these applications is classifying whether a
personal health ev... | 2018-02-26T02:08:28Z | WWW 2018 | null | null | null | null | null | null | null | null | null |
1,803.02155 | Self-Attention with Relative Position Representations | ['Peter Shaw', 'Jakob Uszkoreit', 'Ashish Vaswani'] | ['cs.CL'] | Relying entirely on an attention mechanism, the Transformer introduced by
Vaswani et al. (2017) achieves state-of-the-art results for machine
translation. In contrast to recurrent and convolutional neural networks, it
does not explicitly model relative or absolute position information in its
structure. Instead, it requ... | 2018-03-06T13:13:11Z | NAACL 2018 | null | null | Self-Attention with Relative Position Representations | ['Peter Shaw', 'Jakob Uszkoreit', 'Ashish Vaswani'] | 2,018 | North American Chapter of the Association for Computational Linguistics | 2,317 | 14 | ['Computer Science'] |
1,803.02324 | Annotation Artifacts in Natural Language Inference Data | ['Suchin Gururangan', 'Swabha Swayamdipta', 'Omer Levy', 'Roy Schwartz', 'Samuel R. Bowman', 'Noah A. Smith'] | ['cs.CL', 'cs.AI'] | Large-scale datasets for natural language inference are created by presenting
crowd workers with a sentence (premise), and asking them to generate three new
sentences (hypotheses) that it entails, contradicts, or is logically neutral
with respect to. We show that, in a significant portion of such data, this
protocol le... | 2018-03-06T18:23:08Z | 6 pages, 1 figure, NAACL 2018 | null | null | Annotation Artifacts in Natural Language Inference Data | ['Suchin Gururangan', 'Swabha Swayamdipta', 'Omer Levy', 'Roy Schwartz', 'Samuel R. Bowman', 'Noah A. Smith'] | 2,018 | North American Chapter of the Association for Computational Linguistics | 1,181 | 30 | ['Computer Science'] |
1,803.03835 | Kickstarting Deep Reinforcement Learning | ['Simon Schmitt', 'Jonathan J. Hudson', 'Augustin Zidek', 'Simon Osindero', 'Carl Doersch', 'Wojciech M. Czarnecki', 'Joel Z. Leibo', 'Heinrich Kuttler', 'Andrew Zisserman', 'Karen Simonyan', 'S. M. Ali Eslami'] | ['cs.LG'] | We present a method for using previously-trained 'teacher' agents to
kickstart the training of a new 'student' agent. To this end, we leverage ideas
from policy distillation and population based training. Our method places no
constraints on the architecture of the teacher or student agents, and it
regulates itself to a... | 2018-03-10T16:45:00Z | null | null | null | Kickstarting Deep Reinforcement Learning | ['Simon Schmitt', 'Jonathan J. Hudson', 'Augustin Žídek', 'Simon Osindero', 'Carl Doersch', 'Wojciech M. Czarnecki', 'Joel Z. Leibo', 'Heinrich Küttler', 'Andrew Zisserman', 'K. Simonyan', 'S. Eslami'] | 2,018 | arXiv.org | 135 | 21 | ['Computer Science', 'Mathematics'] |
1,803.04271 | Super-resolution of Sentinel-2 images: Learning a globally applicable
deep neural network | ['Charis Lanaras', 'José Bioucas-Dias', 'Silvano Galliani', 'Emmanuel Baltsavias', 'Konrad Schindler'] | ['cs.CV', 'cs.LG'] | The Sentinel-2 satellite mission delivers multi-spectral imagery with 13
spectral bands, acquired at three different spatial resolutions. The aim of
this research is to super-resolve the lower-resolution (20 m and 60 m Ground
Sampling Distance - GSD) bands to 10 m GSD, so as to obtain a complete data
cube at the maxima... | 2018-03-12T14:15:07Z | 19 pages, 11 figures | ISPRS Journal of Photogrammetry and Remote Sensing, 146 (2018),
pp. 305-319 | 10.1016/j.isprsjprs.2018.09.018 | Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network | ['Charis Lanaras', 'J. Bioucas-Dias', 'Silvano Galliani', 'E. Baltsavias', 'K. Schindler'] | 2,018 | Isprs Journal of Photogrammetry and Remote Sensing | 289 | 50 | ['Computer Science'] |
1,803.04626 | Maintaining Natural Image Statistics with the Contextual Loss | ['Roey Mechrez', 'Itamar Talmi', 'Firas Shama', 'Lihi Zelnik-Manor'] | ['cs.CV'] | Maintaining natural image statistics is a crucial factor in restoration and
generation of realistic looking images. When training CNNs, photorealism is
usually attempted by adversarial training (GAN), that pushes the output images
to lie on the manifold of natural images. GANs are very powerful, but not
perfect. They a... | 2018-03-13T05:19:26Z | null | null | null | null | null | null | null | null | null | null |
1,803.0503 | Deep-FSMN for Large Vocabulary Continuous Speech Recognition | ['Shiliang Zhang', 'Ming Lei', 'Zhijie Yan', 'Lirong Dai'] | ['cs.NE', 'cs.CL'] | In this paper, we present an improved feedforward sequential memory networks
(FSMN) architecture, namely Deep-FSMN (DFSMN), by introducing skip connections
between memory blocks in adjacent layers. These skip connections enable the
information flow across different layers and thus alleviate the gradient
vanishing probl... | 2018-03-04T11:08:16Z | null | null | null | null | null | null | null | null | null | null |
1,803.05337 | Learning to Recognize Musical Genre from Audio | ['Michaël Defferrard', 'Sharada P. Mohanty', 'Sean F. Carroll', 'Marcel Salathé'] | ['cs.SD', 'cs.IR', 'cs.LG', 'eess.AS', 'stat.ML'] | We here summarize our experience running a challenge with open data for
musical genre recognition. Those notes motivate the task and the challenge
design, show some statistics about the submissions, and present the results. | 2018-03-13T15:58:58Z | submitted to WWW'18 after challenge round-1 | null | null | Learning to Recognize Musical Genre from Audio: Challenge Overview | ['Michaël Defferrard', 'S. Mohanty', 'Sean F. Carroll', 'M. Salathé'] | 2,018 | The Web Conference | 20 | 6 | ['Computer Science', 'Engineering', 'Mathematics'] |
1,803.05457 | Think you have Solved Question Answering? Try ARC, the AI2 Reasoning
Challenge | ['Peter Clark', 'Isaac Cowhey', 'Oren Etzioni', 'Tushar Khot', 'Ashish Sabharwal', 'Carissa Schoenick', 'Oyvind Tafjord'] | ['cs.AI', 'cs.CL', 'cs.IR'] | We present a new question set, text corpus, and baselines assembled to
encourage AI research in advanced question answering. Together, these
constitute the AI2 Reasoning Challenge (ARC), which requires far more powerful
knowledge and reasoning than previous challenges such as SQuAD or SNLI. The ARC
question set is part... | 2018-03-14T18:04:21Z | 10 pages, 7 tables, 2 figures | null | null | null | null | null | null | null | null | null |
1,803.06535 | Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks
and Metrics for Formality Style Transfer | ['Sudha Rao', 'Joel Tetreault'] | ['cs.CL'] | Style transfer is the task of automatically transforming a piece of text in
one particular style into another. A major barrier to progress in this field
has been a lack of training and evaluation datasets, as well as benchmarks and
automatic metrics. In this work, we create the largest corpus for a particular
stylistic... | 2018-03-17T16:35:04Z | To appear in the proceedings of North American Chapter of the
Association for Computational Linguistics: Human Language Technologies 2018 | null | null | Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer | ['Sudha Rao', 'Joel R. Tetreault'] | 2,018 | North American Chapter of the Association for Computational Linguistics | 399 | 42 | ['Computer Science'] |
1,803.07474 | An Improved Evaluation Framework for Generative Adversarial Networks | ['Shaohui Liu', 'Yi Wei', 'Jiwen Lu', 'Jie Zhou'] | ['cs.CV'] | In this paper, we propose an improved quantitative evaluation framework for
Generative Adversarial Networks (GANs) on generating domain-specific images,
where we improve conventional evaluation methods on two levels: the feature
representation and the evaluation metric. Unlike most existing evaluation
frameworks which ... | 2018-03-20T15:09:09Z | 21 pages, 9 figures, 8 tables | null | null | An Improved Evaluation Framework for Generative Adversarial Networks | ['Shaohui Liu', 'Yi Wei', 'Jiwen Lu', 'Jie Zhou'] | 2,018 | arXiv.org | 49 | 42 | ['Computer Science'] |
1,803.08225 | PersonLab: Person Pose Estimation and Instance Segmentation with a
Bottom-Up, Part-Based, Geometric Embedding Model | ['George Papandreou', 'Tyler Zhu', 'Liang-Chieh Chen', 'Spyros Gidaris', 'Jonathan Tompson', 'Kevin Murphy'] | ['cs.CV'] | We present a box-free bottom-up approach for the tasks of pose estimation and
instance segmentation of people in multi-person images using an efficient
single-shot model. The proposed PersonLab model tackles both semantic-level
reasoning and object-part associations using part-based modeling. Our model
employs a convol... | 2018-03-22T04:31:02Z | Person detection and pose estimation, segmentation and grouping | null | null | null | null | null | null | null | null | null |
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