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1,910.12592 | BUT System Description to VoxCeleb Speaker Recognition Challenge 2019 | ['Hossein Zeinali', 'Shuai Wang', 'Anna Silnova', 'Pavel Matějka', 'Oldřich Plchot'] | ['eess.AS', 'cs.CL', 'cs.SD'] | In this report, we describe the submission of Brno University of Technology
(BUT) team to the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2019. We also
provide a brief analysis of different systems on VoxCeleb-1 test sets.
Submitted systems for both Fixed and Open conditions are a fusion of 4
Convolutional Neural N... | 2019-10-16T11:27:27Z | null | null | null | BUT System Description to VoxCeleb Speaker Recognition Challenge 2019 | ['Hossein Zeinali', 'Shuai Wang', 'Anna Silnova', 'P. Matejka', 'Oldrich Plchot'] | 2,019 | arXiv.org | 248 | 16 | ['Engineering', 'Computer Science'] |
1,910.1284 | Evaluating the Factual Consistency of Abstractive Text Summarization | ['Wojciech Kryściński', 'Bryan McCann', 'Caiming Xiong', 'Richard Socher'] | ['cs.CL'] | Currently used metrics for assessing summarization algorithms do not account
for whether summaries are factually consistent with source documents. We
propose a weakly-supervised, model-based approach for verifying factual
consistency and identifying conflicts between source documents and a generated
summary. Training d... | 2019-10-28T17:51:44Z | 11 pages, 7 tables, 1 algorithm | null | null | null | null | null | null | null | null | null |
1,910.13267 | BPE-Dropout: Simple and Effective Subword Regularization | ['Ivan Provilkov', 'Dmitrii Emelianenko', 'Elena Voita'] | ['cs.CL'] | Subword segmentation is widely used to address the open vocabulary problem in
machine translation. The dominant approach to subword segmentation is Byte Pair
Encoding (BPE), which keeps the most frequent words intact while splitting the
rare ones into multiple tokens. While multiple segmentations are possible even
with... | 2019-10-29T13:42:56Z | ACL 2020 (camera-ready) | null | null | BPE-Dropout: Simple and Effective Subword Regularization | ['Ivan Provilkov', 'Dmitrii Emelianenko', 'Elena Voita'] | 2,019 | Annual Meeting of the Association for Computational Linguistics | 289 | 31 | ['Computer Science'] |
1,910.13461 | BART: Denoising Sequence-to-Sequence Pre-training for Natural Language
Generation, Translation, and Comprehension | ['Mike Lewis', 'Yinhan Liu', 'Naman Goyal', 'Marjan Ghazvininejad', 'Abdelrahman Mohamed', 'Omer Levy', 'Ves Stoyanov', 'Luke Zettlemoyer'] | ['cs.CL', 'cs.LG', 'stat.ML'] | We present BART, a denoising autoencoder for pretraining sequence-to-sequence
models. BART is trained by (1) corrupting text with an arbitrary noising
function, and (2) learning a model to reconstruct the original text. It uses a
standard Tranformer-based neural machine translation architecture which,
despite its simpl... | 2019-10-29T18:01:00Z | null | null | null | BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension | ['M. Lewis', 'Yinhan Liu', 'Naman Goyal', 'Marjan Ghazvininejad', 'Abdel-rahman Mohamed', 'Omer Levy', 'Veselin Stoyanov', 'Luke Zettlemoyer'] | 2,019 | Annual Meeting of the Association for Computational Linguistics | 10,934 | 36 | ['Computer Science', 'Mathematics'] |
1,910.13793 | Time to Take Emoji Seriously: They Vastly Improve Casual Conversational
Models | ['Pieter Delobelle', 'Bettina Berendt'] | ['cs.CL'] | Graphical emoji are ubiquitous in modern-day online conversations. So is a
single thumbs-up emoji able to signify an agreement, without any words. We
argue that the current state-of-the-art systems are ill-equipped to correctly
interpret these emoji, especially in a conversational context. However, in a
casual context,... | 2019-10-30T12:11:36Z | Accepted at Benelearn 2019 | null | null | Time to Take Emoji Seriously: They Vastly Improve Casual Conversational Models | ['Pieter Delobelle', 'Bettina Berendt'] | 2,019 | BNAIC/BENELEARN | 11 | 37 | ['Computer Science'] |
1,910.14296 | LIMIT-BERT : Linguistic Informed Multi-Task BERT | ['Junru Zhou', 'Zhuosheng Zhang', 'Hai Zhao', 'Shuailiang Zhang'] | ['cs.CL', 'cs.LG'] | In this paper, we present a Linguistic Informed Multi-Task BERT (LIMIT-BERT)
for learning language representations across multiple linguistic tasks by
Multi-Task Learning (MTL). LIMIT-BERT includes five key linguistic syntax and
semantics tasks: Part-Of-Speech (POS) tags, constituent and dependency
syntactic parsing, s... | 2019-10-31T08:14:51Z | EMNLP 2020, ACL Findings | null | null | null | null | null | null | null | null | null |
1,910.14659 | Masked Language Model Scoring | ['Julian Salazar', 'Davis Liang', 'Toan Q. Nguyen', 'Katrin Kirchhoff'] | ['cs.CL', 'cs.LG', 'eess.AS', 'stat.ML'] | Pretrained masked language models (MLMs) require finetuning for most NLP
tasks. Instead, we evaluate MLMs out of the box via their pseudo-log-likelihood
scores (PLLs), which are computed by masking tokens one by one. We show that
PLLs outperform scores from autoregressive language models like GPT-2 in a
variety of task... | 2019-10-31T17:51:21Z | ACL 2020 camera-ready (presented July 2020) | Proceedings of the 58th Annual Meeting of the Association for
Computational Linguistics (2020), 2699-2712 | 10.18653/v1/2020.acl-main.240 | null | null | null | null | null | null | null |
1,911.00536 | DialoGPT: Large-Scale Generative Pre-training for Conversational
Response Generation | ['Yizhe Zhang', 'Siqi Sun', 'Michel Galley', 'Yen-Chun Chen', 'Chris Brockett', 'Xiang Gao', 'Jianfeng Gao', 'Jingjing Liu', 'Bill Dolan'] | ['cs.CL', 'cs.LG'] | We present a large, tunable neural conversational response generation model,
DialoGPT (dialogue generative pre-trained transformer). Trained on 147M
conversation-like exchanges extracted from Reddit comment chains over a period
spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch
transformer to at... | 2019-11-01T18:16:54Z | Accepted by ACL 2020 system demonstration | null | null | DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation | ['Yizhe Zhang', 'Siqi Sun', 'Michel Galley', 'Yen-Chun Chen', 'Chris Brockett', 'Xiang Gao', 'Jianfeng Gao', 'Jingjing Liu', 'W. Dolan'] | 2,019 | Annual Meeting of the Association for Computational Linguistics | 1,529 | 32 | ['Computer Science'] |
1,911.01547 | On the Measure of Intelligence | ['François Chollet'] | ['cs.AI'] | To make deliberate progress towards more intelligent and more human-like
artificial systems, we need to be following an appropriate feedback signal: we
need to be able to define and evaluate intelligence in a way that enables
comparisons between two systems, as well as comparisons with humans. Over the
past hundred yea... | 2019-11-05T00:31:38Z | null | null | null | null | null | null | null | null | null | null |
1,911.02116 | Unsupervised Cross-lingual Representation Learning at Scale | ['Alexis Conneau', 'Kartikay Khandelwal', 'Naman Goyal', 'Vishrav Chaudhary', 'Guillaume Wenzek', 'Francisco Guzmán', 'Edouard Grave', 'Myle Ott', 'Luke Zettlemoyer', 'Veselin Stoyanov'] | ['cs.CL'] | This paper shows that pretraining multilingual language models at scale leads
to significant performance gains for a wide range of cross-lingual transfer
tasks. We train a Transformer-based masked language model on one hundred
languages, using more than two terabytes of filtered CommonCrawl data. Our
model, dubbed XLM-... | 2019-11-05T22:42:00Z | ACL 2020 (+ updated results) | null | null | Unsupervised Cross-lingual Representation Learning at Scale | ['Alexis Conneau', 'Kartikay Khandelwal', 'Naman Goyal', 'Vishrav Chaudhary', 'Guillaume Wenzek', 'Francisco Guzmán', 'Edouard Grave', 'Myle Ott', 'Luke Zettlemoyer', 'Veselin Stoyanov'] | 2,019 | Annual Meeting of the Association for Computational Linguistics | 6,627 | 42 | ['Computer Science'] |
1,911.0215 | Fast Transformer Decoding: One Write-Head is All You Need | ['Noam Shazeer'] | ['cs.NE', 'cs.CL', 'cs.LG'] | Multi-head attention layers, as used in the Transformer neural sequence
model, are a powerful alternative to RNNs for moving information across and
between sequences. While training these layers is generally fast and simple,
due to parallelizability across the length of the sequence, incremental
inference (where such p... | 2019-11-06T00:19:05Z | null | null | null | null | null | null | null | null | null | null |
1,911.02671 | Open Domain Web Keyphrase Extraction Beyond Language Modeling | ['Lee Xiong', 'Chuan Hu', 'Chenyan Xiong', 'Daniel Campos', 'Arnold Overwijk'] | ['cs.CL', 'cs.IR'] | This paper studies keyphrase extraction in real-world scenarios where
documents are from diverse domains and have variant content quality. We curate
and release OpenKP, a large scale open domain keyphrase extraction dataset with
near one hundred thousand web documents and expert keyphrase annotations. To
handle the var... | 2019-11-06T23:12:56Z | null | EMNLP-IJCNLP 2019 | null | null | null | null | null | null | null | null |
1,911.02782 | S2ORC: The Semantic Scholar Open Research Corpus | ['Kyle Lo', 'Lucy Lu Wang', 'Mark Neumann', 'Rodney Kinney', 'Dan S. Weld'] | ['cs.CL', 'cs.DL'] | We introduce S2ORC, a large corpus of 81.1M English-language academic papers
spanning many academic disciplines. The corpus consists of rich metadata, paper
abstracts, resolved bibliographic references, as well as structured full text
for 8.1M open access papers. Full text is annotated with automatically-detected
inlin... | 2019-11-07T07:34:43Z | ACL 2020 | null | null | GORC: A large contextual citation graph of academic papers | ['Kyle Lo', 'Lucy Lu Wang', 'Mark Neumann', 'Rodney Michael Kinney', 'Daniel S. Weld'] | 2,019 | arXiv.org | 10 | 53 | ['Computer Science'] |
1,911.02855 | Dice Loss for Data-imbalanced NLP Tasks | ['Xiaoya Li', 'Xiaofei Sun', 'Yuxian Meng', 'Junjun Liang', 'Fei Wu', 'Jiwei Li'] | ['cs.CL'] | Many NLP tasks such as tagging and machine reading comprehension are faced
with the severe data imbalance issue: negative examples significantly outnumber
positive examples, and the huge number of background examples (or easy-negative
examples) overwhelms the training. The most commonly used cross entropy (CE)
criteria... | 2019-11-07T11:14:05Z | null | null | null | null | null | null | null | null | null | null |
1,911.02969 | BERTs of a feather do not generalize together: Large variability in
generalization across models with similar test set performance | ['R. Thomas McCoy', 'Junghyun Min', 'Tal Linzen'] | ['cs.CL'] | If the same neural network architecture is trained multiple times on the same
dataset, will it make similar linguistic generalizations across runs? To study
this question, we fine-tuned 100 instances of BERT on the Multi-genre Natural
Language Inference (MNLI) dataset and evaluated them on the HANS dataset, which
evalu... | 2019-11-07T16:20:40Z | 11 pages, 7 figures; accepted to the 2020 BlackboxNLP workshop | null | null | null | null | null | null | null | null | null |
1,911.0309 | What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning | ['Jaejun Lee', 'Raphael Tang', 'Jimmy Lin'] | ['cs.CL'] | Pretrained transformer-based language models have achieved state of the art
across countless tasks in natural language processing. These models are highly
expressive, comprising at least a hundred million parameters and a dozen
layers. Recent evidence suggests that only a few of the final layers need to be
fine-tuned f... | 2019-11-08T07:05:20Z | 5 pages | null | null | null | null | null | null | null | null | null |
1,911.03531 | Neural Arabic Text Diacritization: State of the Art Results and a Novel
Approach for Machine Translation | ['Ali Fadel', 'Ibraheem Tuffaha', "Bara' Al-Jawarneh", 'Mahmoud Al-Ayyoub'] | ['cs.CL', 'cs.LG'] | In this work, we present several deep learning models for the automatic
diacritization of Arabic text. Our models are built using two main approaches,
viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN),
with several enhancements such as 100-hot encoding, embeddings, Conditional
Random Field (CRF... | 2019-11-08T20:52:12Z | 18 pages, 17 figures, 14 tables | null | 10.18653/v1/D19-5229 | Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation | ['A. Fadel', 'Ibraheem Tuffaha', "Bara' Al-Jawarneh", 'M. Al-Ayyoub'] | 2,019 | Conference on Empirical Methods in Natural Language Processing | 31 | 27 | ['Computer Science'] |
1,911.03705 | CommonGen: A Constrained Text Generation Challenge for Generative
Commonsense Reasoning | ['Bill Yuchen Lin', 'Wangchunshu Zhou', 'Ming Shen', 'Pei Zhou', 'Chandra Bhagavatula', 'Yejin Choi', 'Xiang Ren'] | ['cs.CL', 'cs.AI', 'cs.CV'] | Recently, large-scale pre-trained language models have demonstrated
impressive performance on several commonsense-reasoning benchmark datasets.
However, building machines with commonsense to compose realistically plausible
sentences remains challenging. In this paper, we present a constrained text
generation task, Comm... | 2019-11-09T14:53:59Z | Accepted to EMNLP 2020 Findings. Add one more human reference for
each test example: Table 1,3 & Figure 4 & Section 3.3, 3.4 are updated.
Project page: https://inklab.usc.edu/CommonGen/ | null | null | CommonGen: A Constrained Text Generation Dataset Towards Generative Commonsense Reasoning | ['Bill Yuchen Lin', 'Ming Shen', 'Yu Xing', 'Pei Zhou', 'Xiang Ren'] | 2,019 | arXiv.org | 16 | 53 | ['Computer Science'] |
1,911.03814 | Scalable Zero-shot Entity Linking with Dense Entity Retrieval | ['Ledell Wu', 'Fabio Petroni', 'Martin Josifoski', 'Sebastian Riedel', 'Luke Zettlemoyer'] | ['cs.CL'] | This paper introduces a conceptually simple, scalable, and highly effective
BERT-based entity linking model, along with an extensive evaluation of its
accuracy-speed trade-off. We present a two-stage zero-shot linking algorithm,
where each entity is defined only by a short textual description. The first
stage does retr... | 2019-11-10T01:01:45Z | accepted at EMNLP 2020 | null | null | Zero-shot Entity Linking with Dense Entity Retrieval | ['Ledell Yu Wu', 'F. Petroni', 'Martin Josifoski', 'Sebastian Riedel', 'Luke Zettlemoyer'] | 2,019 | arXiv.org | 181 | 23 | ['Computer Science'] |
1,911.03854 | r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake
News Detection | ['Kai Nakamura', 'Sharon Levy', 'William Yang Wang'] | ['cs.CL', 'cs.CY', 'cs.IR'] | Fake news has altered society in negative ways in politics and culture. It
has adversely affected both online social network systems as well as offline
communities and conversations. Using automatic machine learning classification
models is an efficient way to combat the widespread dissemination of fake news.
However, ... | 2019-11-10T05:06:38Z | Accepted LREC 2020 | null | null | null | null | null | null | null | null | null |
1,911.03882 | Pre-train and Plug-in: Flexible Conditional Text Generation with
Variational Auto-Encoders | ['Yu Duan', 'Canwen Xu', 'Jiaxin Pei', 'Jialong Han', 'Chenliang Li'] | ['cs.CL', 'cs.LG', 'stat.ML'] | Conditional Text Generation has drawn much attention as a topic of Natural
Language Generation (NLG) which provides the possibility for humans to control
the properties of generated contents. Current conditional generation models
cannot handle emerging conditions due to their joint end-to-end learning
fashion. When a n... | 2019-11-10T09:23:42Z | Accepted as a long paper at ACL 2020 | null | null | Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders | ['Yu Duan', 'Jiaxin Pei', 'Canwen Xu', 'Chenliang Li'] | 2,019 | Annual Meeting of the Association for Computational Linguistics | 43 | 42 | ['Computer Science', 'Mathematics'] |
1,911.03894 | CamemBERT: a Tasty French Language Model | ['Louis Martin', 'Benjamin Muller', 'Pedro Javier Ortiz Suárez', 'Yoann Dupont', 'Laurent Romary', 'Éric Villemonte de la Clergerie', 'Djamé Seddah', 'Benoît Sagot'] | ['cs.CL'] | Pretrained language models are now ubiquitous in Natural Language Processing.
Despite their success, most available models have either been trained on
English data or on the concatenation of data in multiple languages. This makes
practical use of such models --in all languages except English-- very limited.
In this pap... | 2019-11-10T10:46:37Z | ACL 2020 long paper. Web site: https://camembert-model.fr | Proceedings of the 58th Annual Meeting of the Association for
Computational Linguistics, July 2020, Online | 10.18653/v1/2020.acl-main.645 | null | null | null | null | null | null | null |
1,911.04211 | NegBERT: A Transfer Learning Approach for Negation Detection and Scope
Resolution | ['Aditya Khandelwal', 'Suraj Sawant'] | ['cs.CL'] | Negation is an important characteristic of language, and a major component of
information extraction from text. This subtask is of considerable importance to
the biomedical domain. Over the years, multiple approaches have been explored
to address this problem: Rule-based systems, Machine Learning classifiers,
Condition... | 2019-11-11T12:28:29Z | The 12th Language Resources and Evaluation Conference (LREC 2020) | null | null | null | null | null | null | null | null | null |
1,911.04252 | Self-training with Noisy Student improves ImageNet classification | ['Qizhe Xie', 'Minh-Thang Luong', 'Eduard Hovy', 'Quoc V. Le'] | ['cs.LG', 'cs.CV', 'stat.ML'] | We present Noisy Student Training, a semi-supervised learning approach that
works well even when labeled data is abundant. Noisy Student Training achieves
88.4% top-1 accuracy on ImageNet, which is 2.0% better than the
state-of-the-art model that requires 3.5B weakly labeled Instagram images. On
robustness test sets, i... | 2019-11-11T18:59:27Z | CVPR 2020 | null | null | Self-Training With Noisy Student Improves ImageNet Classification | ['Qizhe Xie', 'E. Hovy', 'Minh-Thang Luong', 'Quoc V. Le'] | 2,019 | Computer Vision and Pattern Recognition | 2,398 | 110 | ['Computer Science', 'Mathematics'] |
1,911.04944 | CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB | ['Holger Schwenk', 'Guillaume Wenzek', 'Sergey Edunov', 'Edouard Grave', 'Armand Joulin'] | ['cs.CL'] | We show that margin-based bitext mining in a multilingual sentence space can
be applied to monolingual corpora of billions of sentences. We are using ten
snapshots of a curated common crawl corpus (Wenzek et al., 2019) totalling 32.7
billion unique sentences. Using one unified approach for 38 languages, we were
able to... | 2019-11-10T12:09:46Z | 13 pages, 4 figures. arXiv admin note: text overlap with
arXiv:1907.05791 | null | null | CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web | ['Holger Schwenk', 'Guillaume Wenzek', 'Sergey Edunov', 'Edouard Grave', 'Armand Joulin'] | 2,019 | Annual Meeting of the Association for Computational Linguistics | 263 | 62 | ['Computer Science'] |
1,911.05405 | Identification of Rhetorical Roles of Sentences in Indian Legal
Judgments | ['Paheli Bhattacharya', 'Shounak Paul', 'Kripabandhu Ghosh', 'Saptarshi Ghosh', 'Adam Wyner'] | ['cs.IR'] | Automatically understanding the rhetorical roles of sentences in a legal case
judgement is an important problem to solve, since it can help in several
downstream tasks like summarization of legal judgments, legal search, and so
on. The task is challenging since legal case documents are usually not
well-structured, and ... | 2019-11-13T11:21:20Z | Accepted at the 32nd International Conference on Legal Knowledge and
Information Systems (JURIX) 2019 | null | null | null | null | null | null | null | null | null |
1,911.05507 | Compressive Transformers for Long-Range Sequence Modelling | ['Jack W. Rae', 'Anna Potapenko', 'Siddhant M. Jayakumar', 'Timothy P. Lillicrap'] | ['cs.LG', 'stat.ML'] | We present the Compressive Transformer, an attentive sequence model which
compresses past memories for long-range sequence learning. We find the
Compressive Transformer obtains state-of-the-art language modelling results in
the WikiText-103 and Enwik8 benchmarks, achieving 17.1 ppl and 0.97 bpc
respectively. We also fi... | 2019-11-13T14:36:01Z | 19 pages, 6 figures, 10 tables | null | null | null | null | null | null | null | null | null |
1,911.05722 | Momentum Contrast for Unsupervised Visual Representation Learning | ['Kaiming He', 'Haoqi Fan', 'Yuxin Wu', 'Saining Xie', 'Ross Girshick'] | ['cs.CV'] | We present Momentum Contrast (MoCo) for unsupervised visual representation
learning. From a perspective on contrastive learning as dictionary look-up, we
build a dynamic dictionary with a queue and a moving-averaged encoder. This
enables building a large and consistent dictionary on-the-fly that facilitates
contrastive... | 2019-11-13T18:53:26Z | CVPR 2020 camera-ready. Code:
https://github.com/facebookresearch/moco | null | null | Momentum Contrast for Unsupervised Visual Representation Learning | ['Kaiming He', 'Haoqi Fan', 'Yuxin Wu', 'Saining Xie', 'Ross B. Girshick'] | 2,019 | Computer Vision and Pattern Recognition | 12,184 | 66 | ['Computer Science'] |
1,911.06667 | CenterMask : Real-Time Anchor-Free Instance Segmentation | ['Youngwan Lee', 'Jongyoul Park'] | ['cs.CV'] | We propose a simple yet efficient anchor-free instance segmentation, called
CenterMask, that adds a novel spatial attention-guided mask (SAG-Mask) branch
to anchor-free one stage object detector (FCOS) in the same vein with Mask
R-CNN. Plugged into the FCOS object detector, the SAG-Mask branch predicts a
segmentation m... | 2019-11-15T14:38:12Z | CVPR 2020 | null | null | null | null | null | null | null | null | null |
1,911.07023 | Effectively Unbiased FID and Inception Score and where to find them | ['Min Jin Chong', 'David Forsyth'] | ['cs.CV', 'cs.LG'] | This paper shows that two commonly used evaluation metrics for generative
models, the Fr\'echet Inception Distance (FID) and the Inception Score (IS),
are biased -- the expected value of the score computed for a finite sample set
is not the true value of the score. Worse, the paper shows that the bias term
depends on t... | 2019-11-16T12:54:05Z | CVPR 2020 | null | null | null | null | null | null | null | null | null |
1,911.07067 | ResUNet++: An Advanced Architecture for Medical Image Segmentation | ['Debesh Jha', 'Pia H. Smedsrud', 'Michael A. Riegler', 'Dag Johansen', 'Thomas de Lange', 'Pal Halvorsen', 'Havard D. Johansen'] | ['eess.IV', 'cs.CV'] | Accurate computer-aided polyp detection and segmentation during colonoscopy
examinations can help endoscopists resect abnormal tissue and thereby decrease
chances of polyps growing into cancer. Towards developing a fully automated
model for pixel-wise polyp segmentation, we propose ResUNet++, which is an
improved ResUN... | 2019-11-16T18:04:17Z | 7 pages, 3 figures, 21st IEEE International Symposium on Multimedia | null | null | null | null | null | null | null | null | null |
1,911.0907 | EfficientDet: Scalable and Efficient Object Detection | ['Mingxing Tan', 'Ruoming Pang', 'Quoc V. Le'] | ['cs.CV', 'cs.LG', 'eess.IV'] | Model efficiency has become increasingly important in computer vision. In
this paper, we systematically study neural network architecture design choices
for object detection and propose several key optimizations to improve
efficiency. First, we propose a weighted bi-directional feature pyramid network
(BiFPN), which al... | 2019-11-20T18:16:09Z | CVPR 2020 | Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition (2020) | null | EfficientDet: Scalable and Efficient Object Detection | ['Mingxing Tan', 'Ruoming Pang', 'Quoc V. Le'] | 2,019 | Computer Vision and Pattern Recognition | 5,136 | 45 | ['Computer Science', 'Engineering'] |
1,911.09099 | SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial
Squeeze Modules and Information Blocking Decoder | ['Hyojin Park', 'Lars Lowe Sjösund', 'YoungJoon Yoo', 'Nicolas Monet', 'Jihwan Bang', 'Nojun Kwak'] | ['cs.CV'] | Designing a lightweight and robust portrait segmentation algorithm is an
important task for a wide range of face applications. However, the problem has
been considered as a subset of the object segmentation problem and less handled
in the semantic segmentation field. Obviously, portrait segmentation has its
unique requ... | 2019-11-20T15:39:24Z | https://github.com/HYOJINPARK/ExtPortraitSeg. arXiv admin note: text
overlap with arXiv:1908.03093 | null | null | null | null | null | null | null | null | null |
1,911.09665 | Adversarial Examples Improve Image Recognition | ['Cihang Xie', 'Mingxing Tan', 'Boqing Gong', 'Jiang Wang', 'Alan Yuille', 'Quoc V. Le'] | ['cs.CV'] | Adversarial examples are commonly viewed as a threat to ConvNets. Here we
present an opposite perspective: adversarial examples can be used to improve
image recognition models if harnessed in the right manner. We propose AdvProp,
an enhanced adversarial training scheme which treats adversarial examples as
additional ex... | 2019-11-21T18:53:23Z | CVPR 2020, models are available at
https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet | null | null | null | null | null | null | null | null | null |
1,911.09709 | Automatically Neutralizing Subjective Bias in Text | ['Reid Pryzant', 'Richard Diehl Martinez', 'Nathan Dass', 'Sadao Kurohashi', 'Dan Jurafsky', 'Diyi Yang'] | ['cs.CL', 'cs.AI'] | Texts like news, encyclopedias, and some social media strive for objectivity.
Yet bias in the form of inappropriate subjectivity - introducing attitudes via
framing, presupposing truth, and casting doubt - remains ubiquitous. This kind
of bias erodes our collective trust and fuels social conflict. To address this
issue... | 2019-11-21T19:15:03Z | To appear at AAAI 2020 | null | null | null | null | null | null | null | null | null |
1,911.10436 | ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for
Common Entity Recognition | ['Hannah Smith', 'Zeyu Zhang', 'John Culnan', 'Peter Jansen'] | ['cs.CL'] | Named entity recognition identifies common classes of entities in text, but
these entity labels are generally sparse, limiting utility to downstream tasks.
In this work we present ScienceExamCER, a densely-labeled semantic
classification corpus of 133k mentions in the science exam domain where nearly
all (96%) of conte... | 2019-11-24T00:08:09Z | null | null | null | null | null | null | null | null | null | null |
1,911.10683 | Image-based table recognition: data, model, and evaluation | ['Xu Zhong', 'Elaheh ShafieiBavani', 'Antonio Jimeno Yepes'] | ['cs.CV'] | Important information that relates to a specific topic in a document is often
organized in tabular format to assist readers with information retrieval and
comparison, which may be difficult to provide in natural language. However,
tabular data in unstructured digital documents, e.g., Portable Document Format
(PDF) and ... | 2019-11-25T03:25:03Z | null | null | null | Image-based table recognition: data, model, and evaluation | ['Xu Zhong', 'Elaheh Shafieibavani', 'Antonio Jimeno-Yepes'] | 2,019 | European Conference on Computer Vision | 223 | 42 | ['Computer Science'] |
1,911.11641 | PIQA: Reasoning about Physical Commonsense in Natural Language | ['Yonatan Bisk', 'Rowan Zellers', 'Ronan Le Bras', 'Jianfeng Gao', 'Yejin Choi'] | ['cs.CL', 'cs.AI', 'cs.LG'] | To apply eyeshadow without a brush, should I use a cotton swab or a
toothpick? Questions requiring this kind of physical commonsense pose a
challenge to today's natural language understanding systems. While recent
pretrained models (such as BERT) have made progress on question answering over
more abstract domains - suc... | 2019-11-26T15:31:46Z | AAAI 2020 | null | null | null | null | null | null | null | null | null |
1,911.11763 | SuperGlue: Learning Feature Matching with Graph Neural Networks | ['Paul-Edouard Sarlin', 'Daniel DeTone', 'Tomasz Malisiewicz', 'Andrew Rabinovich'] | ['cs.CV'] | This paper introduces SuperGlue, a neural network that matches two sets of
local features by jointly finding correspondences and rejecting non-matchable
points. Assignments are estimated by solving a differentiable optimal transport
problem, whose costs are predicted by a graph neural network. We introduce a
flexible c... | 2019-11-26T18:57:21Z | Oral at CVPR 2020, with appendix and link to publicly available code | null | null | null | null | null | null | null | null | null |
1,911.11907 | GhostNet: More Features from Cheap Operations | ['Kai Han', 'Yunhe Wang', 'Qi Tian', 'Jianyuan Guo', 'Chunjing Xu', 'Chang Xu'] | ['cs.CV'] | Deploying convolutional neural networks (CNNs) on embedded devices is
difficult due to the limited memory and computation resources. The redundancy
in feature maps is an important characteristic of those successful CNNs, but
has rarely been investigated in neural architecture design. This paper proposes
a novel Ghost m... | 2019-11-27T01:36:42Z | CVPR 2020. Code is available at
https://github.com/huawei-noah/ghostnet | null | null | GhostNet: More Features From Cheap Operations | ['Kai Han', 'Yunhe Wang', 'Qi Tian', 'Jianyuan Guo', 'Chunjing Xu', 'Chang Xu'] | 2,019 | Computer Vision and Pattern Recognition | 2,724 | 72 | ['Computer Science'] |
1,911.11929 | CSPNet: A New Backbone that can Enhance Learning Capability of CNN | ['Chien-Yao Wang', 'Hong-Yuan Mark Liao', 'I-Hau Yeh', 'Yueh-Hua Wu', 'Ping-Yang Chen', 'Jun-Wei Hsieh'] | ['cs.CV'] | Neural networks have enabled state-of-the-art approaches to achieve
incredible results on computer vision tasks such as object detection. However,
such success greatly relies on costly computation resources, which hinders
people with cheap devices from appreciating the advanced technology. In this
paper, we propose Cro... | 2019-11-27T03:15:27Z | null | null | null | null | null | null | null | null | null | null |
1,911.12146 | NorNE: Annotating Named Entities for Norwegian | ['Fredrik Jørgensen', 'Tobias Aasmoe', 'Anne-Stine Ruud Husevåg', 'Lilja Øvrelid', 'Erik Velldal'] | ['cs.CL'] | This paper presents NorNE, a manually annotated corpus of named entities
which extends the annotation of the existing Norwegian Dependency Treebank.
Comprising both of the official standards of written Norwegian (Bokm{\aa}l and
Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of
entity types... | 2019-11-27T13:30:36Z | Accepted for LREC 2020 | null | null | NorNE: Annotating Named Entities for Norwegian | ['Fredrik Jørgensen', 'Tobias Aasmoe', 'Anne-Stine Ruud Husevaag', 'Lilja Ovrelid', 'Erik Velldal'] | 2,019 | International Conference on Language Resources and Evaluation | 32 | 36 | ['Computer Science'] |
1,911.12559 | KPTimes: A Large-Scale Dataset for Keyphrase Generation on News
Documents | ['Ygor Gallina', 'Florian Boudin', 'Béatrice Daille'] | ['cs.IR', 'cs.CL'] | Keyphrase generation is the task of predicting a set of lexical units that
conveys the main content of a source text. Existing datasets for keyphrase
generation are only readily available for the scholarly domain and include
non-expert annotations. In this paper we present KPTimes, a large-scale dataset
of news texts p... | 2019-11-28T07:12:30Z | Accepted at the International Conference on Natural Language
Generation (INLG), 2019 | null | null | null | null | null | null | null | null | null |
1,912.0069 | EduBERT: Pretrained Deep Language Models for Learning Analytics | ['Benjamin Clavié', 'Kobi Gal'] | ['cs.CY', 'cs.AI', 'cs.CL', 'cs.LG'] | The use of large pretrained neural networks to create contextualized word
embeddings has drastically improved performance on several natural language
processing (NLP) tasks. These computationally expensive models have begun to be
applied to domain-specific NLP tasks such as re-hospitalization prediction from
clinical n... | 2019-12-02T11:32:53Z | Accepted for poster presentation at the 10th International Learning
Analytics and Knowledge (LAK20) Conference | null | null | EduBERT: Pretrained Deep Language Models for Learning Analytics | ['Benjamin Clavié', 'K. Gal'] | 2,019 | arXiv.org | 16 | 10 | ['Computer Science'] |
1,912.01603 | Dream to Control: Learning Behaviors by Latent Imagination | ['Danijar Hafner', 'Timothy Lillicrap', 'Jimmy Ba', 'Mohammad Norouzi'] | ['cs.LG', 'cs.AI', 'cs.RO'] | Learned world models summarize an agent's experience to facilitate learning
complex behaviors. While learning world models from high-dimensional sensory
inputs is becoming feasible through deep learning, there are many potential
ways for deriving behaviors from them. We present Dreamer, a reinforcement
learning agent t... | 2019-12-03T18:57:16Z | 9 pages, 12 figures | null | null | Dream to Control: Learning Behaviors by Latent Imagination | ['Danijar Hafner', 'T. Lillicrap', 'Jimmy Ba', 'Mohammad Norouzi'] | 2,019 | International Conference on Learning Representations | 1,378 | 71 | ['Computer Science'] |
1,912.01865 | StarGAN v2: Diverse Image Synthesis for Multiple Domains | ['Yunjey Choi', 'Youngjung Uh', 'Jaejun Yoo', 'Jung-Woo Ha'] | ['cs.CV', 'cs.LG'] | A good image-to-image translation model should learn a mapping between
different visual domains while satisfying the following properties: 1)
diversity of generated images and 2) scalability over multiple domains.
Existing methods address either of the issues, having limited diversity or
multiple models for all domains... | 2019-12-04T09:42:22Z | Accepted to CVPR 2020 | null | null | null | null | null | null | null | null | null |
1,912.02424 | Bridging the Gap Between Anchor-based and Anchor-free Detection via
Adaptive Training Sample Selection | ['Shifeng Zhang', 'Cheng Chi', 'Yongqiang Yao', 'Zhen Lei', 'Stan Z. Li'] | ['cs.CV'] | Object detection has been dominated by anchor-based detectors for several
years. Recently, anchor-free detectors have become popular due to the proposal
of FPN and Focal Loss. In this paper, we first point out that the essential
difference between anchor-based and anchor-free detection is actually how to
define positiv... | 2019-12-05T07:49:56Z | Accepted by CVPR 2020 as Oral; Best Paper Nomination | null | null | Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection | ['Shifeng Zhang', 'Cheng Chi', 'Yongqiang Yao', 'Zhen Lei', 'Stan Z. Li'] | 2,019 | Computer Vision and Pattern Recognition | 1,566 | 74 | ['Computer Science'] |
1,912.04958 | Analyzing and Improving the Image Quality of StyleGAN | ['Tero Karras', 'Samuli Laine', 'Miika Aittala', 'Janne Hellsten', 'Jaakko Lehtinen', 'Timo Aila'] | ['cs.CV', 'cs.LG', 'cs.NE', 'eess.IV', 'stat.ML'] | The style-based GAN architecture (StyleGAN) yields state-of-the-art results
in data-driven unconditional generative image modeling. We expose and analyze
several of its characteristic artifacts, and propose changes in both model
architecture and training methods to address them. In particular, we redesign
the generator... | 2019-12-03T11:44:01Z | null | null | null | null | null | null | null | null | null | null |
1,912.05007 | Oktoberfest Food Dataset | ['Alexander Ziller', 'Julius Hansjakob', 'Vitalii Rusinov', 'Daniel Zügner', 'Peter Vogel', 'Stephan Günnemann'] | ['cs.CV', 'cs.LG', 'stat.ML'] | We release a realistic, diverse, and challenging dataset for object detection
on images. The data was recorded at a beer tent in Germany and consists of 15
different categories of food and drink items. We created more than 2,500 object
annotations by hand for 1,110 images captured by a video camera above the
checkout. ... | 2019-11-22T09:28:59Z | Dataset publication of Oktoberfest Food Dataset. 4 pages, 6 figures | null | null | Oktoberfest Food Dataset | ['Alexander Ziller', 'Julius Hansjakob', 'Vitalii Rusinov', 'Daniel Zügner', 'P. Vogel', 'Stephan Günnemann'] | 2,019 | arXiv.org | 7 | 10 | ['Computer Science', 'Mathematics'] |
1,912.05027 | SpineNet: Learning Scale-Permuted Backbone for Recognition and
Localization | ['Xianzhi Du', 'Tsung-Yi Lin', 'Pengchong Jin', 'Golnaz Ghiasi', 'Mingxing Tan', 'Yin Cui', 'Quoc V. Le', 'Xiaodan Song'] | ['cs.CV', 'cs.LG', 'eess.IV'] | Convolutional neural networks typically encode an input image into a series
of intermediate features with decreasing resolutions. While this structure is
suited to classification tasks, it does not perform well for tasks requiring
simultaneous recognition and localization (e.g., object detection). The
encoder-decoder a... | 2019-12-10T22:13:42Z | CVPR 2020 | null | null | null | null | null | null | null | null | null |
1,912.0667 | Common Voice: A Massively-Multilingual Speech Corpus | ['Rosana Ardila', 'Megan Branson', 'Kelly Davis', 'Michael Henretty', 'Michael Kohler', 'Josh Meyer', 'Reuben Morais', 'Lindsay Saunders', 'Francis M. Tyers', 'Gregor Weber'] | ['cs.CL', 'cs.LG'] | The Common Voice corpus is a massively-multilingual collection of transcribed
speech intended for speech technology research and development. Common Voice is
designed for Automatic Speech Recognition purposes but can be useful in other
domains (e.g. language identification). To achieve scale and sustainability,
the Com... | 2019-12-13T19:22:44Z | Accepted to LREC 2020 | null | null | null | null | null | null | null | null | null |
1,912.07076 | Multilingual is not enough: BERT for Finnish | ['Antti Virtanen', 'Jenna Kanerva', 'Rami Ilo', 'Jouni Luoma', 'Juhani Luotolahti', 'Tapio Salakoski', 'Filip Ginter', 'Sampo Pyysalo'] | ['cs.CL'] | Deep learning-based language models pretrained on large unannotated text
corpora have been demonstrated to allow efficient transfer learning for natural
language processing, with recent approaches such as the transformer-based BERT
model advancing the state of the art across a variety of tasks. While most work
on these... | 2019-12-15T17:50:56Z | null | null | null | null | null | null | null | null | null | null |
1,912.07726 | Towards Fairer Datasets: Filtering and Balancing the Distribution of the
People Subtree in the ImageNet Hierarchy | ['Kaiyu Yang', 'Klint Qinami', 'Li Fei-Fei', 'Jia Deng', 'Olga Russakovsky'] | ['cs.CV'] | Computer vision technology is being used by many but remains representative
of only a few. People have reported misbehavior of computer vision models,
including offensive prediction results and lower performance for
underrepresented groups. Current computer vision models are typically developed
using datasets consistin... | 2019-12-16T22:03:05Z | Accepted to FAT* 2020 | null | 10.1145/3351095.3375709 | Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy | ['Kaiyu Yang', 'Klint Qinami', 'Li Fei-Fei', 'Jia Deng', 'Olga Russakovsky'] | 2,019 | FAT* | 325 | 87 | ['Computer Science'] |
1,912.07875 | Libri-Light: A Benchmark for ASR with Limited or No Supervision | ['Jacob Kahn', 'Morgane Rivière', 'Weiyi Zheng', 'Evgeny Kharitonov', 'Qiantong Xu', 'Pierre-Emmanuel Mazaré', 'Julien Karadayi', 'Vitaliy Liptchinsky', 'Ronan Collobert', 'Christian Fuegen', 'Tatiana Likhomanenko', 'Gabriel Synnaeve', 'Armand Joulin', 'Abdelrahman Mohamed', 'Emmanuel Dupoux'] | ['cs.CL', 'cs.SD', 'eess.AS'] | We introduce a new collection of spoken English audio suitable for training
speech recognition systems under limited or no supervision. It is derived from
open-source audio books from the LibriVox project. It contains over 60K hours
of audio, which is, to our knowledge, the largest freely-available corpus of
speech. Th... | 2019-12-17T08:47:30Z | null | null | 10.1109/ICASSP40776.2020.9052942 | null | null | null | null | null | null | null |
1,912.08777 | PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive
Summarization | ['Jingqing Zhang', 'Yao Zhao', 'Mohammad Saleh', 'Peter J. Liu'] | ['cs.CL'] | Recent work pre-training Transformers with self-supervised objectives on
large text corpora has shown great success when fine-tuned on downstream NLP
tasks including text summarization. However, pre-training objectives tailored
for abstractive text summarization have not been explored. Furthermore there is
a lack of sy... | 2019-12-18T18:16:20Z | Added results from mixed+stochastic model, test-set overlapping
analysis; Code link added; Accepted for ICML 2020. arXiv admin note: text
overlap with arXiv:1605.06560, arXiv:1205.2395, arXiv:0902.4351,
arXiv:1610.09932, arXiv:nucl-ex/0512029 by other authors | null | null | PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization | ['Jingqing Zhang', 'Yao Zhao', 'Mohammad Saleh', 'Peter J. Liu'] | 2,019 | International Conference on Machine Learning | 2,059 | 58 | ['Computer Science'] |
1,912.09363 | Temporal Fusion Transformers for Interpretable Multi-horizon Time Series
Forecasting | ['Bryan Lim', 'Sercan O. Arik', 'Nicolas Loeff', 'Tomas Pfister'] | ['stat.ML', 'cs.LG'] | Multi-horizon forecasting problems often contain a complex mix of inputs --
including static (i.e. time-invariant) covariates, known future inputs, and
other exogenous time series that are only observed historically -- without any
prior information on how they interact with the target. While several deep
learning model... | 2019-12-19T16:45:40Z | null | null | null | null | null | null | null | null | null | null |
1,912.09582 | BERTje: A Dutch BERT Model | ['Wietse de Vries', 'Andreas van Cranenburgh', 'Arianna Bisazza', 'Tommaso Caselli', 'Gertjan van Noord', 'Malvina Nissim'] | ['cs.CL'] | The transformer-based pre-trained language model BERT has helped to improve
state-of-the-art performance on many natural language processing (NLP) tasks.
Using the same architecture and parameters, we developed and evaluated a
monolingual Dutch BERT model called BERTje. Compared to the multilingual BERT
model, which in... | 2019-12-19T22:59:26Z | null | null | null | null | null | null | null | null | null | null |
1,912.09723 | SberQuAD -- Russian Reading Comprehension Dataset: Description and
Analysis | ['Pavel Efimov', 'Andrey Chertok', 'Leonid Boytsov', 'Pavel Braslavski'] | ['cs.CL'] | SberQuAD -- a large scale analog of Stanford SQuAD in the Russian language -
is a valuable resource that has not been properly presented to the scientific
community. We fill this gap by providing a description, a thorough analysis,
and baseline experimental results. | 2019-12-20T09:44:42Z | null | null | 10.1007/978-3-030-58219-7_1 | SberQuAD - Russian Reading Comprehension Dataset: Description and Analysis | ['Pavel Efimov', 'Andrey Chertok', 'Leonid Boytsov', 'Pavel Braslavski'] | 2,019 | Conference and Labs of the Evaluation Forum | 61 | 41 | ['Computer Science'] |
1,912.10205 | Decoupled Attention Network for Text Recognition | ['Tianwei Wang', 'Yuanzhi Zhu', 'Lianwen Jin', 'Canjie Luo', 'Xiaoxue Chen', 'Yaqiang Wu', 'Qianying Wang', 'Mingxiang Cai'] | ['cs.CV'] | Text recognition has attracted considerable research interests because of its
various applications. The cutting-edge text recognition methods are based on
attention mechanisms. However, most of attention methods usually suffer from
serious alignment problem due to its recurrency alignment operation, where the
alignment... | 2019-12-21T05:51:58Z | 9 pages, 8 figures, 6 tables, accepted by AAAI-2020 | null | null | Decoupled Attention Network for Text Recognition | ['Tianwei Wang', 'Yuanzhi Zhu', 'Lianwen Jin', 'Canjie Luo', 'Xiaoxue Chen', 'Y. Wu', 'Qianying Wang', 'Mingxiang Cai'] | 2,019 | AAAI Conference on Artificial Intelligence | 255 | 49 | ['Computer Science'] |
1,912.10211 | PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern
Recognition | ['Qiuqiang Kong', 'Yin Cao', 'Turab Iqbal', 'Yuxuan Wang', 'Wenwu Wang', 'Mark D. Plumbley'] | ['cs.SD', 'eess.AS'] | Audio pattern recognition is an important research topic in the machine
learning area, and includes several tasks such as audio tagging, acoustic scene
classification, music classification, speech emotion classification and sound
event detection. Recently, neural networks have been applied to tackle audio
pattern recog... | 2019-12-21T06:53:14Z | 14 pages | null | null | null | null | null | null | null | null | null |
1,912.10389 | Lessons from Archives: Strategies for Collecting Sociocultural Data in
Machine Learning | ['Eun Seo Jo', 'Timnit Gebru'] | ['cs.LG', 'cs.AI', 'cs.CY', 'I.2.0'] | A growing body of work shows that many problems in fairness, accountability,
transparency, and ethics in machine learning systems are rooted in decisions
surrounding the data collection and annotation process. In spite of its
fundamental nature however, data collection remains an overlooked part of the
machine learning... | 2019-12-22T05:56:55Z | To be published in Conference on Fairness, Accountability, and
Transparency FAT* '20, January 27-30, 2020, Barcelona, Spain. ACM, New York,
NY, USA, 11 pages | null | 10.1145/3351095.3372829 | Lessons from archives: strategies for collecting sociocultural data in machine learning | ['Eun Seo Jo', 'Timnit Gebru'] | 2,019 | FAT* | 317 | 66 | ['Computer Science'] |
1,912.10458 | Emotion Recognition from Speech | ['Kannan Venkataramanan', 'Haresh Rengaraj Rajamohan'] | ['cs.SD', 'cs.CL', 'eess.AS'] | In this work, we conduct an extensive comparison of various approaches to
speech based emotion recognition systems. The analyses were carried out on
audio recordings from Ryerson Audio-Visual Database of Emotional Speech and
Song (RAVDESS). After pre-processing the raw audio files, features such as
Log-Mel Spectrogram,... | 2019-12-22T14:43:14Z | null | null | null | Emotion Recognition from Speech | ['Kannan Venkataramanan', 'H. Rajamohan'] | 2,019 | arXiv.org | 15 | 26 | ['Computer Science', 'Engineering'] |
1,912.1137 | Big Transfer (BiT): General Visual Representation Learning | ['Alexander Kolesnikov', 'Lucas Beyer', 'Xiaohua Zhai', 'Joan Puigcerver', 'Jessica Yung', 'Sylvain Gelly', 'Neil Houlsby'] | ['cs.CV', 'cs.LG'] | Transfer of pre-trained representations improves sample efficiency and
simplifies hyperparameter tuning when training deep neural networks for vision.
We revisit the paradigm of pre-training on large supervised datasets and
fine-tuning the model on a target task. We scale up pre-training, and propose a
simple recipe th... | 2019-12-24T14:04:11Z | The first three authors contributed equally. Results on ObjectNet are
reported in v3 | null | null | null | null | null | null | null | null | null |
1,912.12142 | Lung and Colon Cancer Histopathological Image Dataset (LC25000) | ['Andrew A. Borkowski', 'Marilyn M. Bui', 'L. Brannon Thomas', 'Catherine P. Wilson', 'Lauren A. DeLand', 'Stephen M. Mastorides'] | ['eess.IV', 'cs.CV', 'q-bio.QM'] | The field of Machine Learning, a subset of Artificial Intelligence, has led
to remarkable advancements in many areas, including medicine. Machine Learning
algorithms require large datasets to train computer models successfully.
Although there are medical image datasets available, more image datasets are
needed from a v... | 2019-12-16T16:28:00Z | 2 pages | null | null | null | null | null | null | null | null | null |
1,912.1218 | Axial Attention in Multidimensional Transformers | ['Jonathan Ho', 'Nal Kalchbrenner', 'Dirk Weissenborn', 'Tim Salimans'] | ['cs.CV'] | We propose Axial Transformers, a self-attention-based autoregressive model
for images and other data organized as high dimensional tensors. Existing
autoregressive models either suffer from excessively large computational
resource requirements for high dimensional data, or make compromises in terms
of distribution expr... | 2019-12-20T13:27:27Z | 10 pages | null | null | null | null | null | null | null | null | null |
1,912.13318 | LayoutLM: Pre-training of Text and Layout for Document Image
Understanding | ['Yiheng Xu', 'Minghao Li', 'Lei Cui', 'Shaohan Huang', 'Furu Wei', 'Ming Zhou'] | ['cs.CL'] | Pre-training techniques have been verified successfully in a variety of NLP
tasks in recent years. Despite the widespread use of pre-training models for
NLP applications, they almost exclusively focus on text-level manipulation,
while neglecting layout and style information that is vital for document image
understandin... | 2019-12-31T14:31:29Z | KDD 2020 | null | 10.1145/3394486.3403172 | null | null | null | null | null | null | null |
1,912.1344 | Approximate Inference for Fully Bayesian Gaussian Process Regression | ['Vidhi Lalchand', 'Carl Edward Rasmussen'] | ['stat.ML', 'cs.LG'] | Learning in Gaussian Process models occurs through the adaptation of
hyperparameters of the mean and the covariance function. The classical approach
entails maximizing the marginal likelihood yielding fixed point estimates (an
approach called \textit{Type II maximum likelihood} or ML-II). An alternative
learning proced... | 2019-12-31T17:18:48Z | Presented at 2nd Symposium on Advances in Approximate Bayesian
Inference 2019 | Proceedings of Machine Learning Research, Volume 118 (2019) 1-12 | null | null | null | null | null | null | null | null |
2,001.02943 | Binary and Multitask Classification Model for Dutch Anaphora Resolution:
Die/Dat Prediction | ['Liesbeth Allein', 'Artuur Leeuwenberg', 'Marie-Francine Moens'] | ['cs.CL'] | The correct use of Dutch pronouns 'die' and 'dat' is a stumbling block for
both native and non-native speakers of Dutch due to the multiplicity of
syntactic functions and the dependency on the antecedent's gender and number.
Drawing on previous research conducted on neural context-dependent dt-mistake
correction models... | 2020-01-09T12:34:01Z | null | Computational Linguistics in the Netherlands Journal, 10, 19-36
(2020) | null | null | null | null | null | null | null | null |
2,001.03653 | Towards GAN Benchmarks Which Require Generalization | ['Ishaan Gulrajani', 'Colin Raffel', 'Luke Metz'] | ['cs.LG', 'stat.ML'] | For many evaluation metrics commonly used as benchmarks for unconditional
image generation, trivially memorizing the training set attains a better score
than models which are considered state-of-the-art; we consider this
problematic. We clarify a necessary condition for an evaluation metric not to
behave this way: esti... | 2020-01-10T20:18:47Z | ICLR 2019 conference paper | null | null | null | null | null | null | null | null | null |
2,001.04063 | ProphetNet: Predicting Future N-gram for Sequence-to-Sequence
Pre-training | ['Weizhen Qi', 'Yu Yan', 'Yeyun Gong', 'Dayiheng Liu', 'Nan Duan', 'Jiusheng Chen', 'Ruofei Zhang', 'Ming Zhou'] | ['cs.CL'] | This paper presents a new sequence-to-sequence pre-training model called
ProphetNet, which introduces a novel self-supervised objective named future
n-gram prediction and the proposed n-stream self-attention mechanism. Instead
of optimizing one-step-ahead prediction in the traditional sequence-to-sequence
model, the Pr... | 2020-01-13T05:12:38Z | Accepted to EMNLP 2020 Findings. Project page:
https://github.com/microsoft/ProphetNet | null | null | ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training | ['Yu Yan', 'Weizhen Qi', 'Yeyun Gong', 'Dayiheng Liu', 'Nan Duan', 'Jiusheng Chen', 'Ruofei Zhang', 'Ming Zhou'] | 2,020 | Findings | 450 | 50 | ['Computer Science'] |
2,001.04351 | CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark
for Chinese | ['Liang Xu', 'Yu tong', 'Qianqian Dong', 'Yixuan Liao', 'Cong Yu', 'Yin Tian', 'Weitang Liu', 'Lu Li', 'Caiquan Liu', 'Xuanwei Zhang'] | ['cs.CL', 'cs.IR', 'cs.LG'] | In this paper, we introduce the NER dataset from CLUE organization
(CLUENER2020), a well-defined fine-grained dataset for named entity recognition
in Chinese. CLUENER2020 contains 10 categories. Apart from common labels like
person, organization, and location, it contains more diverse categories. It is
more challenging... | 2020-01-13T15:39:56Z | 6 pages, 5 tables, 1 figure | null | null | null | null | null | null | null | null | null |
2,001.04643 | DDSP: Differentiable Digital Signal Processing | ['Jesse Engel', 'Lamtharn Hantrakul', 'Chenjie Gu', 'Adam Roberts'] | ['cs.LG', 'cs.SD', 'eess.AS', 'eess.SP', 'stat.ML'] | Most generative models of audio directly generate samples in one of two
domains: time or frequency. While sufficient to express any signal, these
representations are inefficient, as they do not utilize existing knowledge of
how sound is generated and perceived. A third approach (vocoders/synthesizers)
successfully inco... | 2020-01-14T06:49:37Z | null | null | null | DDSP: Differentiable Digital Signal Processing | ['Jesse Engel', 'Lamtharn Hantrakul', 'Chenjie Gu', 'Adam Roberts'] | 2,020 | International Conference on Learning Representations | 381 | 41 | ['Computer Science', 'Engineering', 'Mathematics'] |
2,001.06286 | RobBERT: a Dutch RoBERTa-based Language Model | ['Pieter Delobelle', 'Thomas Winters', 'Bettina Berendt'] | ['cs.CL', 'cs.LG'] | Pre-trained language models have been dominating the field of natural
language processing in recent years, and have led to significant performance
gains for various complex natural language tasks. One of the most prominent
pre-trained language models is BERT, which was released as an English as well
as a multilingual v... | 2020-01-17T13:25:44Z | 11 pages, 4 tables, 3 figures. Accepted in EMNLP Findings | null | null | null | null | null | null | null | null | null |
2,001.07487 | Raiders of the Lost Kek: 3.5 Years of Augmented 4chan Posts from the
Politically Incorrect Board | ['Antonis Papasavva', 'Savvas Zannettou', 'Emiliano De Cristofaro', 'Gianluca Stringhini', 'Jeremy Blackburn'] | ['cs.CY', 'cs.SI'] | This paper presents a dataset with over 3.3M threads and 134.5M posts from
the Politically Incorrect board (/pol/) of the imageboard forum 4chan, posted
over a period of almost 3.5 years (June 2016-November 2019). To the best of our
knowledge, this represents the largest publicly available 4chan dataset,
providing the ... | 2020-01-21T12:52:24Z | null | Published at the 14th International AAAI Conference on Web and
Social Media (ICWSM 2020). Please cite the ICWSM version | null | Raiders of the Lost Kek: 3.5 Years of Augmented 4chan Posts from the Politically Incorrect Board | ['Antonis Papasavva', 'Savvas Zannettou', 'Emiliano De Cristofaro', 'G. Stringhini', 'Jeremy Blackburn'] | 2,020 | International Conference on Web and Social Media | 94 | 46 | ['Computer Science'] |
2,001.0821 | Multilingual Denoising Pre-training for Neural Machine Translation | ['Yinhan Liu', 'Jiatao Gu', 'Naman Goyal', 'Xian Li', 'Sergey Edunov', 'Marjan Ghazvininejad', 'Mike Lewis', 'Luke Zettlemoyer'] | ['cs.CL'] | This paper demonstrates that multilingual denoising pre-training produces
significant performance gains across a wide variety of machine translation (MT)
tasks. We present mBART -- a sequence-to-sequence denoising auto-encoder
pre-trained on large-scale monolingual corpora in many languages using the BART
objective. mB... | 2020-01-22T18:59:17Z | Work in progress | null | null | null | null | null | null | null | null | null |
2,001.08361 | Scaling Laws for Neural Language Models | ['Jared Kaplan', 'Sam McCandlish', 'Tom Henighan', 'Tom B. Brown', 'Benjamin Chess', 'Rewon Child', 'Scott Gray', 'Alec Radford', 'Jeffrey Wu', 'Dario Amodei'] | ['cs.LG', 'stat.ML'] | We study empirical scaling laws for language model performance on the
cross-entropy loss. The loss scales as a power-law with model size, dataset
size, and the amount of compute used for training, with some trends spanning
more than seven orders of magnitude. Other architectural details such as
network width or depth h... | 2020-01-23T03:59:20Z | 19 pages, 15 figures | null | null | Scaling Laws for Neural Language Models | ['J. Kaplan', 'Sam McCandlish', 'T. Henighan', 'Tom B. Brown', 'Benjamin Chess', 'R. Child', 'Scott Gray', 'Alec Radford', 'Jeff Wu', 'Dario Amodei'] | 2,020 | arXiv.org | 4,948 | 59 | ['Computer Science', 'Mathematics'] |
2,001.09694 | Retrospective Reader for Machine Reading Comprehension | ['Zhuosheng Zhang', 'Junjie Yang', 'Hai Zhao'] | ['cs.CL', 'cs.AI', 'cs.IR'] | Machine reading comprehension (MRC) is an AI challenge that requires machine
to determine the correct answers to questions based on a given passage. MRC
systems must not only answer question when necessary but also distinguish when
no answer is available according to the given passage and then tactfully
abstain from an... | 2020-01-27T11:14:34Z | Accepted by AAAI 2021 | null | null | Retrospective Reader for Machine Reading Comprehension | ['Zhuosheng Zhang', 'Junjie Yang', 'Hai Zhao'] | 2,020 | AAAI Conference on Artificial Intelligence | 227 | 57 | ['Computer Science'] |
2,001.09977 | Towards a Human-like Open-Domain Chatbot | ['Daniel Adiwardana', 'Minh-Thang Luong', 'David R. So', 'Jamie Hall', 'Noah Fiedel', 'Romal Thoppilan', 'Zi Yang', 'Apoorv Kulshreshtha', 'Gaurav Nemade', 'Yifeng Lu', 'Quoc V. Le'] | ['cs.CL', 'cs.LG', 'cs.NE', 'stat.ML'] | We present Meena, a multi-turn open-domain chatbot trained end-to-end on data
mined and filtered from public domain social media conversations. This 2.6B
parameter neural network is simply trained to minimize perplexity of the next
token. We also propose a human evaluation metric called Sensibleness and
Specificity Ave... | 2020-01-27T18:53:15Z | 38 pages, 12 figures | null | null | null | null | null | null | null | null | null |
2,001.1119 | 2018 Robotic Scene Segmentation Challenge | ['Max Allan', 'Satoshi Kondo', 'Sebastian Bodenstedt', 'Stefan Leger', 'Rahim Kadkhodamohammadi', 'Imanol Luengo', 'Felix Fuentes', 'Evangello Flouty', 'Ahmed Mohammed', 'Marius Pedersen', 'Avinash Kori', 'Varghese Alex', 'Ganapathy Krishnamurthi', 'David Rauber', 'Robert Mendel', 'Christoph Palm', 'Sophia Bano', 'Guin... | ['cs.CV', 'cs.RO'] | In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich
using endoscope images of ex-vivo tissue with automatically generated
annotations from robot forward kinematics and instrument CAD models. However,
the limited background variation and simple motion rendered the dataset
uninformative in learni... | 2020-01-30T06:37:07Z | null | null | null | null | null | null | null | null | null | null |
2,001.11314 | ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework
for Natural Language Generation | ['Dongling Xiao', 'Han Zhang', 'Yukun Li', 'Yu Sun', 'Hao Tian', 'Hua Wu', 'Haifeng Wang'] | ['cs.CL', 'cs.LG'] | Current pre-training works in natural language generation pay little
attention to the problem of exposure bias on downstream tasks. To address this
issue, we propose an enhanced multi-flow sequence to sequence pre-training and
fine-tuning framework named ERNIE-GEN, which bridges the discrepancy between
training and inf... | 2020-01-26T02:54:49Z | The source codes and pre-trained models have been released at
https://github.com/PaddlePaddle/ERNIE. We have also updated the performances
of ERNIE-GEN under a larger scaled pre-training corpora in appendix A | null | null | null | null | null | null | null | null | null |
2,002.00212 | Pop Music Transformer: Beat-based Modeling and Generation of Expressive
Pop Piano Compositions | ['Yu-Siang Huang', 'Yi-Hsuan Yang'] | ['cs.SD', 'cs.AI', 'eess.AS', 'stat.ML'] | A great number of deep learning based models have been recently proposed for
automatic music composition. Among these models, the Transformer stands out as
a prominent approach for generating expressive classical piano performance with
a coherent structure of up to one minute. The model is powerful in that it
learns ab... | 2020-02-01T14:12:35Z | Accepted at ACM Multimedia 2020 | null | null | Pop Music Transformer: Generating Music with Rhythm and Harmony | ['Yu-Siang Huang', 'Yi-Hsuan Yang'] | 2,020 | arXiv.org | 39 | 32 | ['Computer Science', 'Engineering', 'Mathematics'] |
2,002.00293 | Beat the AI: Investigating Adversarial Human Annotation for Reading
Comprehension | ['Max Bartolo', 'Alastair Roberts', 'Johannes Welbl', 'Sebastian Riedel', 'Pontus Stenetorp'] | ['cs.CL'] | Innovations in annotation methodology have been a catalyst for Reading
Comprehension (RC) datasets and models. One recent trend to challenge current
RC models is to involve a model in the annotation process: humans create
questions adversarially, such that the model fails to answer them correctly. In
this work we inves... | 2020-02-02T00:22:55Z | null | Transactions of the Association for Computational Linguistics,
Volume 8, 2020 p.662-678 | 10.1162/tacl_a_00338 | Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension | ['Max Bartolo', 'A. Roberts', 'Johannes Welbl', 'Sebastian Riedel', 'Pontus Stenetorp'] | 2,020 | Transactions of the Association for Computational Linguistics | 175 | 58 | ['Computer Science'] |
2,002.01322 | Training Keyword Spotters with Limited and Synthesized Speech Data | ['James Lin', 'Kevin Kilgour', 'Dominik Roblek', 'Matthew Sharifi'] | ['eess.AS', 'cs.LG', 'cs.SD', 'stat.ML'] | With the rise of low power speech-enabled devices, there is a growing demand
to quickly produce models for recognizing arbitrary sets of keywords. As with
many machine learning tasks, one of the most challenging parts in the model
creation process is obtaining a sufficient amount of training data. In this
paper, we exp... | 2020-01-31T07:50:42Z | null | null | null | null | null | null | null | null | null | null |
2,002.01808 | K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters | ['Ruize Wang', 'Duyu Tang', 'Nan Duan', 'Zhongyu Wei', 'Xuanjing Huang', 'Jianshu ji', 'Guihong Cao', 'Daxin Jiang', 'Ming Zhou'] | ['cs.CL', 'cs.LG'] | We study the problem of injecting knowledge into large pre-trained models
like BERT and RoBERTa. Existing methods typically update the original
parameters of pre-trained models when injecting knowledge. However, when
multiple kinds of knowledge are injected, the historically injected knowledge
would be flushed away. To... | 2020-02-05T14:30:49Z | null | null | null | K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters | ['Ruize Wang', 'Duyu Tang', 'Nan Duan', 'Zhongyu Wei', 'Xuanjing Huang', 'Jianshu Ji', 'Guihong Cao', 'Daxin Jiang', 'Ming Zhou'] | 2,020 | Findings | 557 | 53 | ['Computer Science'] |
2,002.02497 | On the limits of cross-domain generalization in automated X-ray
prediction | ['Joseph Paul Cohen', 'Mohammad Hashir', 'Rupert Brooks', 'Hadrien Bertrand'] | ['eess.IV', 'cs.LG', 'q-bio.QM', 'stat.ML'] | This large scale study focuses on quantifying what X-rays diagnostic
prediction tasks generalize well across multiple different datasets. We present
evidence that the issue of generalization is not due to a shift in the images
but instead a shift in the labels. We study the cross-domain performance,
agreement between m... | 2020-02-06T20:07:54Z | Full paper at MIDL2020 | null | null | On the limits of cross-domain generalization in automated X-ray prediction | ['Joseph Paul Cohen', 'Mohammad Hashir', 'Rupert Brooks', 'H. Bertrand'] | 2,020 | International Conference on Medical Imaging with Deep Learning | 130 | 39 | ['Computer Science', 'Physics', 'Engineering', 'Biology', 'Mathematics'] |
2,002.02925 | BERT-of-Theseus: Compressing BERT by Progressive Module Replacing | ['Canwen Xu', 'Wangchunshu Zhou', 'Tao Ge', 'Furu Wei', 'Ming Zhou'] | ['cs.CL', 'cs.LG'] | In this paper, we propose a novel model compression approach to effectively
compress BERT by progressive module replacing. Our approach first divides the
original BERT into several modules and builds their compact substitutes. Then,
we randomly replace the original modules with their substitutes to train the
compact mo... | 2020-02-07T17:52:16Z | EMNLP 2020 | null | null | null | null | null | null | null | null | null |
2,002.04745 | On Layer Normalization in the Transformer Architecture | ['Ruibin Xiong', 'Yunchang Yang', 'Di He', 'Kai Zheng', 'Shuxin Zheng', 'Chen Xing', 'Huishuai Zhang', 'Yanyan Lan', 'Liwei Wang', 'Tie-Yan Liu'] | ['cs.LG', 'cs.CL', 'stat.ML'] | The Transformer is widely used in natural language processing tasks. To train
a Transformer however, one usually needs a carefully designed learning rate
warm-up stage, which is shown to be crucial to the final performance but will
slow down the optimization and bring more hyper-parameter tunings. In this
paper, we fir... | 2020-02-12T00:33:03Z | null | Published on ICML 2020 | null | null | null | null | null | null | null | null |
2,002.04815 | Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis
and Natural Language Inference | ['Youwei Song', 'Jiahai Wang', 'Zhiwei Liang', 'Zhiyue Liu', 'Tao Jiang'] | ['cs.CL', 'cs.LG'] | Aspect based sentiment analysis aims to identify the sentimental tendency
towards a given aspect in text. Fine-tuning of pretrained BERT performs
excellent on this task and achieves state-of-the-art performances. Existing
BERT-based works only utilize the last output layer of BERT and ignore the
semantic knowledge in t... | 2020-02-12T06:11:48Z | 5 pages, 2 figures | null | null | null | null | null | null | null | null | null |
2,002.05202 | GLU Variants Improve Transformer | ['Noam Shazeer'] | ['cs.LG', 'cs.NE', 'stat.ML'] | Gated Linear Units (arXiv:1612.08083) consist of the component-wise product
of two linear projections, one of which is first passed through a sigmoid
function. Variations on GLU are possible, using different nonlinear (or even
linear) functions in place of sigmoid. We test these variants in the
feed-forward sublayers o... | 2020-02-12T19:57:13Z | null | null | null | null | null | null | null | null | null | null |
2,002.05709 | A Simple Framework for Contrastive Learning of Visual Representations | ['Ting Chen', 'Simon Kornblith', 'Mohammad Norouzi', 'Geoffrey Hinton'] | ['cs.LG', 'cs.CV', 'stat.ML'] | This paper presents SimCLR: a simple framework for contrastive learning of
visual representations. We simplify recently proposed contrastive
self-supervised learning algorithms without requiring specialized architectures
or a memory bank. In order to understand what enables the contrastive
prediction tasks to learn use... | 2020-02-13T18:50:45Z | ICML'2020. Code and pretrained models at
https://github.com/google-research/simclr | null | null | null | null | null | null | null | null | null |
2,002.0581 | RNA Secondary Structure Prediction By Learning Unrolled Algorithms | ['Xinshi Chen', 'Yu Li', 'Ramzan Umarov', 'Xin Gao', 'Le Song'] | ['cs.LG', 'stat.ML'] | In this paper, we propose an end-to-end deep learning model, called E2Efold,
for RNA secondary structure prediction which can effectively take into account
the inherent constraints in the problem. The key idea of E2Efold is to directly
predict the RNA base-pairing matrix, and use an unrolled algorithm for
constrained p... | 2020-02-13T23:21:25Z | International Conference on Learning Representations 2020 | International Conference on Learning Representations 2020,
https://openreview.net/forum?id=S1eALyrYDH | null | RNA Secondary Structure Prediction By Learning Unrolled Algorithms | ['Xinshi Chen', 'Yu Li', 'Ramzan Umarov', 'Xin Gao', 'Le Song'] | 2,020 | International Conference on Learning Representations | 119 | 39 | ['Computer Science', 'Mathematics'] |
2,002.06071 | FQuAD: French Question Answering Dataset | ["Martin d'Hoffschmidt", 'Wacim Belblidia', 'Tom Brendlé', 'Quentin Heinrich', 'Maxime Vidal'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Recent advances in the field of language modeling have improved
state-of-the-art results on many Natural Language Processing tasks. Among them,
Reading Comprehension has made significant progress over the past few years.
However, most results are reported in English since labeled resources available
in other languages,... | 2020-02-14T15:23:38Z | 15 pages, 5 figures | null | null | FQuAD: French Question Answering Dataset | ["Martin d'Hoffschmidt", 'Maxime Vidal', 'Wacim Belblidia', 'Quentin Heinrich', "Tom Brendl'e"] | 2,020 | Findings | 100 | 39 | ['Computer Science'] |
2,002.07651 | Listwise Learning to Rank with Deep Q-Networks | ['Abhishek Sharma'] | ['cs.LG', 'cs.IR'] | Learning to Rank is the problem involved with ranking a sequence of documents
based on their relevance to a given query. Deep Q-Learning has been shown to be
a useful method for training an agent in sequential decision making. In this
paper, we show that DeepQRank, our deep q-learning to rank agent, demonstrates
perfor... | 2020-02-13T22:45:56Z | null | null | null | Listwise Learning to Rank with Deep Q-Networks | ['Abhishek Sharma'] | 2,020 | arXiv.org | 1 | 10 | ['Computer Science'] |
2,002.08155 | CodeBERT: A Pre-Trained Model for Programming and Natural Languages | ['Zhangyin Feng', 'Daya Guo', 'Duyu Tang', 'Nan Duan', 'Xiaocheng Feng', 'Ming Gong', 'Linjun Shou', 'Bing Qin', 'Ting Liu', 'Daxin Jiang', 'Ming Zhou'] | ['cs.CL', 'cs.PL'] | We present CodeBERT, a bimodal pre-trained model for programming language
(PL) and nat-ural language (NL). CodeBERT learns general-purpose
representations that support downstream NL-PL applications such as natural
language codesearch, code documentation generation, etc. We develop CodeBERT
with Transformer-based neural... | 2020-02-19T13:09:07Z | Accepted to Findings of EMNLP 2020. 12 pages | null | null | null | null | null | null | null | null | null |
2,002.08258 | Knapsack Pruning with Inner Distillation | ['Yonathan Aflalo', 'Asaf Noy', 'Ming Lin', 'Itamar Friedman', 'Lihi Zelnik'] | ['cs.LG', 'stat.ML'] | Neural network pruning reduces the computational cost of an
over-parameterized network to improve its efficiency. Popular methods vary from
$\ell_1$-norm sparsification to Neural Architecture Search (NAS). In this work,
we propose a novel pruning method that optimizes the final accuracy of the
pruned network and distil... | 2020-02-19T16:04:48Z | null | null | null | Knapsack Pruning with Inner Distillation | ['Y. Aflalo', 'Asaf Noy', 'Ming Lin', 'Itamar Friedman', 'Lihi Zelnik-Manor'] | 2,020 | arXiv.org | 34 | 57 | ['Computer Science', 'Mathematics'] |
2,002.08653 | Detecting Code Clones with Graph Neural Networkand Flow-Augmented
Abstract Syntax Tree | ['Wenhan Wang', 'Ge Li', 'Bo Ma', 'Xin Xia', 'Zhi Jin'] | ['cs.SE', 'cs.AI'] | Code clones are semantically similar code fragments pairs that are
syntactically similar or different. Detection of code clones can help to reduce
the cost of software maintenance and prevent bugs. Numerous approaches of
detecting code clones have been proposed previously, but most of them focus on
detecting syntactic ... | 2020-02-20T10:18:37Z | Accepted by SANER 2020 | null | null | null | null | null | null | null | null | null |
2,002.08909 | REALM: Retrieval-Augmented Language Model Pre-Training | ['Kelvin Guu', 'Kenton Lee', 'Zora Tung', 'Panupong Pasupat', 'Ming-Wei Chang'] | ['cs.CL', 'cs.LG'] | Language model pre-training has been shown to capture a surprising amount of
world knowledge, crucial for NLP tasks such as question answering. However,
this knowledge is stored implicitly in the parameters of a neural network,
requiring ever-larger networks to cover more facts.
To capture knowledge in a more modular... | 2020-02-10T18:40:59Z | null | null | null | REALM: Retrieval-Augmented Language Model Pre-Training | ['Kelvin Guu', 'Kenton Lee', 'Zora Tung', 'Panupong Pasupat', 'Ming-Wei Chang'] | 2,020 | International Conference on Machine Learning | 2,133 | 43 | ['Computer Science'] |
2,002.0891 | How Much Knowledge Can You Pack Into the Parameters of a Language Model? | ['Adam Roberts', 'Colin Raffel', 'Noam Shazeer'] | ['cs.CL', 'cs.LG', 'stat.ML'] | It has recently been observed that neural language models trained on
unstructured text can implicitly store and retrieve knowledge using natural
language queries. In this short paper, we measure the practical utility of this
approach by fine-tuning pre-trained models to answer questions without access
to any external c... | 2020-02-10T18:55:58Z | Camera-ready version for EMNLP | null | null | How Much Knowledge Can You Pack into the Parameters of a Language Model? | ['Adam Roberts', 'Colin Raffel', 'Noam M. Shazeer'] | 2,020 | Conference on Empirical Methods in Natural Language Processing | 898 | 40 | ['Computer Science', 'Mathematics'] |
2,002.09018 | Scalable Second Order Optimization for Deep Learning | ['Rohan Anil', 'Vineet Gupta', 'Tomer Koren', 'Kevin Regan', 'Yoram Singer'] | ['cs.LG', 'math.OC', 'stat.ML'] | Optimization in machine learning, both theoretical and applied, is presently
dominated by first-order gradient methods such as stochastic gradient descent.
Second-order optimization methods, that involve second derivatives and/or
second order statistics of the data, are far less prevalent despite strong
theoretical pro... | 2020-02-20T20:51:33Z | 24 pages, Code available here: https://bit.ly/3uXXtKy | null | null | null | null | null | null | null | null | null |
2,002.09219 | Stochastic Latent Residual Video Prediction | ['Jean-Yves Franceschi', 'Edouard Delasalles', 'Mickaël Chen', 'Sylvain Lamprier', 'Patrick Gallinari'] | ['cs.CV', 'cs.LG', 'stat.ML'] | Designing video prediction models that account for the inherent uncertainty
of the future is challenging. Most works in the literature are based on
stochastic image-autoregressive recurrent networks, which raises several
performance and applicability issues. An alternative is to use fully latent
temporal models which u... | 2020-02-21T10:44:01Z | null | Thirty-seventh International Conference on Machine Learning,
International Machine Learning Society, Jul 2020, Vienne, Austria. pp.89--102 | null | null | null | null | null | null | null | null |
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