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2,005.05957 | Flowtron: an Autoregressive Flow-based Generative Network for
Text-to-Speech Synthesis | ['Rafael Valle', 'Kevin Shih', 'Ryan Prenger', 'Bryan Catanzaro'] | ['cs.SD', 'cs.CL', 'cs.LG', 'eess.AS'] | In this paper we propose Flowtron: an autoregressive flow-based generative
network for text-to-speech synthesis with control over speech variation and
style transfer. Flowtron borrows insights from IAF and revamps Tacotron in
order to provide high-quality and expressive mel-spectrogram synthesis.
Flowtron is optimized ... | 2020-05-12T17:57:17Z | 10 pages, 7 pictures | null | null | null | null | null | null | null | null | null |
2,005.06149 | DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses | ['Yaxin Li', 'Wei Jin', 'Han Xu', 'Jiliang Tang'] | ['cs.LG', 'cs.CR', 'stat.ML'] | DeepRobust is a PyTorch adversarial learning library which aims to build a
comprehensive and easy-to-use platform to foster this research field. It
currently contains more than 10 attack algorithms and 8 defense algorithms in
image domain and 9 attack algorithms and 4 defense algorithms in graph domain,
under a variety... | 2020-05-13T04:43:46Z | Adversarial attacks and defenses, Pytorch library | null | null | null | null | null | null | null | null | null |
2,005.07143 | ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in
TDNN Based Speaker Verification | ['Brecht Desplanques', 'Jenthe Thienpondt', 'Kris Demuynck'] | ['eess.AS', 'cs.SD'] | Current speaker verification techniques rely on a neural network to extract
speaker representations. The successful x-vector architecture is a Time Delay
Neural Network (TDNN) that applies statistics pooling to project
variable-length utterances into fixed-length speaker characterizing embeddings.
In this paper, we pro... | 2020-05-14T17:02:15Z | proceedings of INTERSPEECH 2020 | null | 10.21437/Interspeech.2020-2650 | ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification | ['Brecht Desplanques', 'Jenthe Thienpondt', 'Kris Demuynck'] | 2,020 | Interspeech | 1,350 | 31 | ['Computer Science', 'Engineering'] |
2,005.07202 | Pre-training technique to localize medical BERT and enhance biomedical
BERT | ['Shoya Wada', 'Toshihiro Takeda', 'Shiro Manabe', 'Shozo Konishi', 'Jun Kamohara', 'Yasushi Matsumura'] | ['cs.CL'] | Pre-training large-scale neural language models on raw texts has made a
significant contribution to improving transfer learning in natural language
processing (NLP). With the introduction of transformer-based language models,
such as bidirectional encoder representations from transformers (BERT), the
performance of inf... | 2020-05-14T18:00:01Z | We made the pre-trained weights of ouBioBERT and the source code for
fine-tuning freely available at
https://github.com/sy-wada/blue_benchmark_with_transformers | null | 10.1016/j.artmed.2024.102889 | Oversampling effect in pretraining for bidirectional encoder representations from transformers (BERT) to localize medical BERT and enhance biomedical BERT | ['Shoya Wada', 'Toshihiro Takeda', 'Katsuki Okada', 'S. Manabe', 'Shozo Konishi', 'Jun Kamohara', 'Y. Matsumura'] | 2,020 | Artif. Intell. Medicine | 12 | 38 | ['Medicine', 'Computer Science'] |
2,005.07421 | Spelling Error Correction with Soft-Masked BERT | ['Shaohua Zhang', 'Haoran Huang', 'Jicong Liu', 'Hang Li'] | ['cs.CL', 'cs.LG'] | Spelling error correction is an important yet challenging task because a
satisfactory solution of it essentially needs human-level language
understanding ability. Without loss of generality we consider Chinese spelling
error correction (CSC) in this paper. A state-of-the-art method for the task
selects a character from... | 2020-05-15T09:02:38Z | To be published at ACL 2020 | null | null | Spelling Error Correction with Soft-Masked BERT | ['Shaohua Zhang', 'Haoran Huang', 'Jicong Liu', 'Hang Li'] | 2,020 | Annual Meeting of the Association for Computational Linguistics | 214 | 17 | ['Computer Science'] |
2,005.07503 | COVID-Twitter-BERT: A Natural Language Processing Model to Analyse
COVID-19 Content on Twitter | ['Martin Müller', 'Marcel Salathé', 'Per E Kummervold'] | ['cs.CL', 'cs.LG', 'cs.SI'] | In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based
model, pretrained on a large corpus of Twitter messages on the topic of
COVID-19. Our model shows a 10-30% marginal improvement compared to its base
model, BERT-Large, on five different classification datasets. The largest
improvements are on th... | 2020-05-15T12:40:46Z | null | null | null | null | null | null | null | null | null | null |
2,005.07683 | Movement Pruning: Adaptive Sparsity by Fine-Tuning | ['Victor Sanh', 'Thomas Wolf', 'Alexander M. Rush'] | ['cs.CL', 'cs.LG'] | Magnitude pruning is a widely used strategy for reducing model size in pure
supervised learning; however, it is less effective in the transfer learning
regime that has become standard for state-of-the-art natural language
processing applications. We propose the use of movement pruning, a simple,
deterministic first-ord... | 2020-05-15T17:54:15Z | 14 pages, 6 figures, 3 tables. Published at NeurIPS2020. Code:
\url{huggingface.co/mvp} | null | null | null | null | null | null | null | null | null |
2,005.08072 | Speech Recognition and Multi-Speaker Diarization of Long Conversations | ['Huanru Henry Mao', 'Shuyang Li', 'Julian McAuley', 'Garrison Cottrell'] | ['eess.AS', 'cs.LG', 'cs.SD'] | Speech recognition (ASR) and speaker diarization (SD) models have
traditionally been trained separately to produce rich conversation transcripts
with speaker labels. Recent advances have shown that joint ASR and SD models
can learn to leverage audio-lexical inter-dependencies to improve word
diarization performance. We... | 2020-05-16T19:29:33Z | null | null | null | null | null | null | null | null | null | null |
2,005.081 | Conformer: Convolution-augmented Transformer for Speech Recognition | ['Anmol Gulati', 'James Qin', 'Chung-Cheng Chiu', 'Niki Parmar', 'Yu Zhang', 'Jiahui Yu', 'Wei Han', 'Shibo Wang', 'Zhengdong Zhang', 'Yonghui Wu', 'Ruoming Pang'] | ['eess.AS', 'cs.LG', 'cs.SD'] | Recently Transformer and Convolution neural network (CNN) based models have
shown promising results in Automatic Speech Recognition (ASR), outperforming
Recurrent neural networks (RNNs). Transformer models are good at capturing
content-based global interactions, while CNNs exploit local features
effectively. In this wo... | 2020-05-16T20:56:25Z | Submitted to Interspeech 2020 | null | null | null | null | null | null | null | null | null |
2,005.09007 | U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object
Detection | ['Xuebin Qin', 'Zichen Zhang', 'Chenyang Huang', 'Masood Dehghan', 'Osmar R. Zaiane', 'Martin Jagersand'] | ['cs.CV'] | In this paper, we design a simple yet powerful deep network architecture,
U$^2$-Net, for salient object detection (SOD). The architecture of our
U$^2$-Net is a two-level nested U-structure. The design has the following
advantages: (1) it is able to capture more contextual information from
different scales thanks to the... | 2020-05-18T18:08:26Z | Accepted in Pattern Recognition 2020 | null | 10.1016/j.patcog.2020.107404 | U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection | ['Xuebin Qin', 'Zichen Zhang', 'Chenyang Huang', 'Masood Dehghan', 'Osmar R Zaiane', 'Martin Jägersand'] | 2,020 | Pattern Recognition | 1,683 | 59 | ['Computer Science'] |
2,005.11129 | Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment
Search | ['Jaehyeon Kim', 'Sungwon Kim', 'Jungil Kong', 'Sungroh Yoon'] | ['eess.AS', 'cs.SD'] | Recently, text-to-speech (TTS) models such as FastSpeech and ParaNet have
been proposed to generate mel-spectrograms from text in parallel. Despite the
advantage, the parallel TTS models cannot be trained without guidance from
autoregressive TTS models as their external aligners. In this work, we propose
Glow-TTS, a fl... | 2020-05-22T12:06:46Z | Accepted by NeurIPS2020 | null | null | null | null | null | null | null | null | null |
2,005.11401 | Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks | ['Patrick Lewis', 'Ethan Perez', 'Aleksandra Piktus', 'Fabio Petroni', 'Vladimir Karpukhin', 'Naman Goyal', 'Heinrich Küttler', 'Mike Lewis', 'Wen-tau Yih', 'Tim Rocktäschel', 'Sebastian Riedel', 'Douwe Kiela'] | ['cs.CL', 'cs.LG'] | Large pre-trained language models have been shown to store factual knowledge
in their parameters, and achieve state-of-the-art results when fine-tuned on
downstream NLP tasks. However, their ability to access and precisely manipulate
knowledge is still limited, and hence on knowledge-intensive tasks, their
performance ... | 2020-05-22T21:34:34Z | Accepted at NeurIPS 2020 | null | null | Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks | ['Patrick Lewis', 'Ethan Perez', 'Aleksandara Piktus', 'F. Petroni', 'Vladimir Karpukhin', 'Naman Goyal', 'Heinrich Kuttler', 'M. Lewis', 'Wen-tau Yih', 'Tim Rocktäschel', 'Sebastian Riedel', 'Douwe Kiela'] | 2,020 | Neural Information Processing Systems | 6,575 | 67 | ['Computer Science'] |
2,005.11723 | Query Resolution for Conversational Search with Limited Supervision | ['Nikos Voskarides', 'Dan Li', 'Pengjie Ren', 'Evangelos Kanoulas', 'Maarten de Rijke'] | ['cs.IR', 'cs.CL'] | In this work we focus on multi-turn passage retrieval as a crucial component
of conversational search. One of the key challenges in multi-turn passage
retrieval comes from the fact that the current turn query is often
underspecified due to zero anaphora, topic change, or topic return. Context
from the conversational hi... | 2020-05-24T11:37:22Z | SIGIR 2020 full conference paper | null | 10.1145/3397271.3401130 | null | null | null | null | null | null | null |
2,005.1232 | SCAN: Learning to Classify Images without Labels | ['Wouter Van Gansbeke', 'Simon Vandenhende', 'Stamatios Georgoulis', 'Marc Proesmans', 'Luc Van Gool'] | ['cs.CV', 'cs.LG'] | Can we automatically group images into semantically meaningful clusters when
ground-truth annotations are absent? The task of unsupervised image
classification remains an important, and open challenge in computer vision.
Several recent approaches have tried to tackle this problem in an end-to-end
fashion. In this paper... | 2020-05-25T18:12:33Z | Accepted at ECCV 2020. Includes supplementary. Code and pretrained
models at https://github.com/wvangansbeke/Unsupervised-Classification | null | null | null | null | null | null | null | null | null |
2,005.12515 | ParsBERT: Transformer-based Model for Persian Language Understanding | ['Mehrdad Farahani', 'Mohammad Gharachorloo', 'Marzieh Farahani', 'Mohammad Manthouri'] | ['cs.CL'] | The surge of pre-trained language models has begun a new era in the field of
Natural Language Processing (NLP) by allowing us to build powerful language
models. Among these models, Transformer-based models such as BERT have become
increasingly popular due to their state-of-the-art performance. However, these
models are... | 2020-05-26T05:05:32Z | 10 pages, 5 figures, 7 tables, table 7 corrected and some refs
related to table 7 | null | 10.1007/s11063-021-10528-4 | null | null | null | null | null | null | null |
2,005.12872 | End-to-End Object Detection with Transformers | ['Nicolas Carion', 'Francisco Massa', 'Gabriel Synnaeve', 'Nicolas Usunier', 'Alexander Kirillov', 'Sergey Zagoruyko'] | ['cs.CV'] | We present a new method that views object detection as a direct set
prediction problem. Our approach streamlines the detection pipeline,
effectively removing the need for many hand-designed components like a
non-maximum suppression procedure or anchor generation that explicitly encode
our prior knowledge about the task... | 2020-05-26T17:06:38Z | null | null | null | End-to-End Object Detection with Transformers | ['Nicolas Carion', 'Francisco Massa', 'Gabriel Synnaeve', 'Nicolas Usunier', 'Alexander Kirillov', 'Sergey Zagoruyko'] | 2,020 | European Conference on Computer Vision | 13,239 | 53 | ['Computer Science'] |
2,005.14147 | IMDb data from Two Generations, from 1979 to 2019; Part one, Dataset
Introduction and Preliminary Analysis | ['M. Bahraminasr', 'A. Vafaei Sadr'] | ['cs.CY'] | "IMDb" as a user-regulating and one the most-visited portal has provided an
opportunity to create an enormous database. Analysis of the information on
Internet Movie Database - IMDb, either those related to the movie or provided
by users would help to reveal the determinative factors in the route of success
for each mo... | 2020-05-28T17:01:06Z | 12 pages, 9 figures | null | null | null | null | null | null | null | null | null |
2,005.14165 | Language Models are Few-Shot Learners | ['Tom B. Brown', 'Benjamin Mann', 'Nick Ryder', 'Melanie Subbiah', 'Jared Kaplan', 'Prafulla Dhariwal', 'Arvind Neelakantan', 'Pranav Shyam', 'Girish Sastry', 'Amanda Askell', 'Sandhini Agarwal', 'Ariel Herbert-Voss', 'Gretchen Krueger', 'Tom Henighan', 'Rewon Child', 'Aditya Ramesh', 'Daniel M. Ziegler', 'Jeffrey Wu',... | ['cs.CL'] | Recent work has demonstrated substantial gains on many NLP tasks and
benchmarks by pre-training on a large corpus of text followed by fine-tuning on
a specific task. While typically task-agnostic in architecture, this method
still requires task-specific fine-tuning datasets of thousands or tens of
thousands of examples... | 2020-05-28T17:29:03Z | 40+32 pages | null | null | null | null | null | null | null | null | null |
2,005.14511 | NuClick: A Deep Learning Framework for Interactive Segmentation of
Microscopy Images | ['Navid Alemi Koohbanani', 'Mostafa Jahanifar', 'Neda Zamani Tajadin', 'Nasir Rajpoot'] | ['cs.CV', 'stat.AP'] | Object segmentation is an important step in the workflow of computational
pathology. Deep learning based models generally require large amount of labeled
data for precise and reliable prediction. However, collecting labeled data is
expensive because it often requires expert knowledge, particularly in medical
imaging do... | 2020-05-29T11:51:27Z | null | null | null | NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images | ['Navid Alemi Koohbanani', 'M. Jahanifar', 'Neda Zamani Tajadin', 'N. Rajpoot'] | 2,020 | Medical Image Anal. | 125 | 97 | ['Computer Science', 'Medicine', 'Mathematics'] |
2,006.00885 | CoAID: COVID-19 Healthcare Misinformation Dataset | ['Limeng Cui', 'Dongwon Lee'] | ['cs.SI', 'cs.CL'] | As the COVID-19 virus quickly spreads around the world, unfortunately,
misinformation related to COVID-19 also gets created and spreads like wild
fire. Such misinformation has caused confusion among people, disruptions in
society, and even deadly consequences in health problems. To be able to
understand, detect, and mi... | 2020-05-22T19:08:14Z | null | null | null | null | null | null | null | null | null | null |
2,006.02049 | FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining | ['Xiaoliang Dai', 'Alvin Wan', 'Peizhao Zhang', 'Bichen Wu', 'Zijian He', 'Zhen Wei', 'Kan Chen', 'Yuandong Tian', 'Matthew Yu', 'Peter Vajda', 'Joseph E. Gonzalez'] | ['cs.CV', 'cs.LG', 'cs.NE'] | Neural Architecture Search (NAS) yields state-of-the-art neural networks that
outperform their best manually-designed counterparts. However, previous NAS
methods search for architectures under one set of training hyper-parameters
(i.e., a training recipe), overlooking superior architecture-recipe
combinations. To addre... | 2020-06-03T05:20:21Z | null | null | null | FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function | ['Xiaoliang Dai', 'Alvin Wan', 'Peizhao Zhang', 'Bichen Wu', 'Zijian He', 'Zhen Wei', 'Kan Chen', 'Yuandong Tian', 'Matthew Yu', 'Péter Vajda', 'Joseph E. Gonzalez'] | 2,020 | arXiv.org | 73 | 54 | ['Computer Science'] |
2,006.03236 | Funnel-Transformer: Filtering out Sequential Redundancy for Efficient
Language Processing | ['Zihang Dai', 'Guokun Lai', 'Yiming Yang', 'Quoc V. Le'] | ['cs.LG', 'cs.CL', 'stat.ML'] | With the success of language pretraining, it is highly desirable to develop
more efficient architectures of good scalability that can exploit the abundant
unlabeled data at a lower cost. To improve the efficiency, we examine the
much-overlooked redundancy in maintaining a full-length token-level
presentation, especiall... | 2020-06-05T05:16:23Z | null | null | null | Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing | ['Zihang Dai', 'Guokun Lai', 'Yiming Yang', 'Quoc V. Le'] | 2,020 | Neural Information Processing Systems | 236 | 44 | ['Computer Science', 'Mathematics'] |
2,006.03654 | DeBERTa: Decoding-enhanced BERT with Disentangled Attention | ['Pengcheng He', 'Xiaodong Liu', 'Jianfeng Gao', 'Weizhu Chen'] | ['cs.CL', 'cs.LG', 'cs.CL, cs.GL', 'I.2; I.7'] | Recent progress in pre-trained neural language models has significantly
improved the performance of many natural language processing (NLP) tasks. In
this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT
with disentangled attention) that improves the BERT and RoBERTa models using
two novel techn... | 2020-06-05T19:54:34Z | 20 pages,5 figures, 13 tables. In v2, we scale up DeBERTa to 1.5B
parameters and it surpasses the human performance on SuperGLUE leaderboard
for the first time as of December 29, 2020. In v3, we replace MLM with RTD
objective which significantly improves the model performance | null | null | null | null | null | null | null | null | null |
2,006.03659 | DeCLUTR: Deep Contrastive Learning for Unsupervised Textual
Representations | ['John Giorgi', 'Osvald Nitski', 'Bo Wang', 'Gary Bader'] | ['cs.CL', 'cs.LG'] | Sentence embeddings are an important component of many natural language
processing (NLP) systems. Like word embeddings, sentence embeddings are
typically learned on large text corpora and then transferred to various
downstream tasks, such as clustering and retrieval. Unlike word embeddings, the
highest performing solut... | 2020-06-05T20:00:28Z | ACL2021 Camera Ready V2 | null | null | DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations | ['John Giorgi', 'O. Nitski', 'Gary D Bader', 'Bo Wang'] | 2,020 | Annual Meeting of the Association for Computational Linguistics | 499 | 97 | ['Computer Science'] |
2,006.03677 | Visual Transformers: Token-based Image Representation and Processing for
Computer Vision | ['Bichen Wu', 'Chenfeng Xu', 'Xiaoliang Dai', 'Alvin Wan', 'Peizhao Zhang', 'Zhicheng Yan', 'Masayoshi Tomizuka', 'Joseph Gonzalez', 'Kurt Keutzer', 'Peter Vajda'] | ['cs.CV', 'cs.LG', 'eess.IV'] | Computer vision has achieved remarkable success by (a) representing images as
uniformly-arranged pixel arrays and (b) convolving highly-localized features.
However, convolutions treat all image pixels equally regardless of importance;
explicitly model all concepts across all images, regardless of content; and
struggle ... | 2020-06-05T20:49:49Z | null | null | null | null | null | null | null | null | null | null |
2,006.04045 | A Generic First-Order Algorithmic Framework for Bi-Level Programming
Beyond Lower-Level Singleton | ['Risheng Liu', 'Pan Mu', 'Xiaoming Yuan', 'Shangzhi Zeng', 'Jin Zhang'] | ['cs.LG', 'cs.CV', 'math.DS', 'math.OC', 'stat.ML'] | In recent years, a variety of gradient-based first-order methods have been
developed to solve bi-level optimization problems for learning applications.
However, theoretical guarantees of these existing approaches heavily rely on
the simplification that for each fixed upper-level variable, the lower-level
solution must ... | 2020-06-07T05:18:50Z | Accepted at ICML 2020 | null | null | null | null | null | null | null | null | null |
2,006.04558 | FastSpeech 2: Fast and High-Quality End-to-End Text to Speech | ['Yi Ren', 'Chenxu Hu', 'Xu Tan', 'Tao Qin', 'Sheng Zhao', 'Zhou Zhao', 'Tie-Yan Liu'] | ['eess.AS', 'cs.CL', 'cs.LG', 'cs.SD'] | Non-autoregressive text to speech (TTS) models such as FastSpeech can
synthesize speech significantly faster than previous autoregressive models with
comparable quality. The training of FastSpeech model relies on an
autoregressive teacher model for duration prediction (to provide more
information as input) and knowledg... | 2020-06-08T13:05:40Z | Accepted by ICLR 2021 | null | null | null | null | null | null | null | null | null |
2,006.06676 | Training Generative Adversarial Networks with Limited Data | ['Tero Karras', 'Miika Aittala', 'Janne Hellsten', 'Samuli Laine', 'Jaakko Lehtinen', 'Timo Aila'] | ['cs.CV', 'cs.LG', 'cs.NE', 'stat.ML'] | Training generative adversarial networks (GAN) using too little data
typically leads to discriminator overfitting, causing training to diverge. We
propose an adaptive discriminator augmentation mechanism that significantly
stabilizes training in limited data regimes. The approach does not require
changes to loss functi... | 2020-06-11T17:06:34Z | null | null | null | Training Generative Adversarial Networks with Limited Data | ['Tero Karras', 'M. Aittala', 'Janne Hellsten', 'S. Laine', 'J. Lehtinen', 'Timo Aila'] | 2,020 | Neural Information Processing Systems | 1,897 | 56 | ['Computer Science', 'Mathematics'] |
2,006.06687 | On the asymptotics of wide networks with polynomial activations | ['Kyle Aitken', 'Guy Gur-Ari'] | ['cs.LG', 'hep-th', 'stat.ML'] | We consider an existing conjecture addressing the asymptotic behavior of
neural networks in the large width limit. The results that follow from this
conjecture include tight bounds on the behavior of wide networks during
stochastic gradient descent, and a derivation of their finite-width dynamics.
We prove the conjectu... | 2020-06-11T18:00:01Z | 8+12 pages, 6 figures, 2 tables | null | null | On the asymptotics of wide networks with polynomial activations | ['Kyle Aitken', 'Guy Gur-Ari'] | 2,020 | arXiv.org | 23 | 17 | ['Computer Science', 'Physics', 'Mathematics'] |
2,006.06873 | FastPitch: Parallel Text-to-speech with Pitch Prediction | ['Adrian Łańcucki'] | ['eess.AS', 'cs.CL', 'cs.LG', 'cs.SD'] | We present FastPitch, a fully-parallel text-to-speech model based on
FastSpeech, conditioned on fundamental frequency contours. The model predicts
pitch contours during inference. By altering these predictions, the generated
speech can be more expressive, better match the semantic of the utterance, and
in the end more ... | 2020-06-11T23:23:58Z | Accepted to ICASSP 2021 | null | null | null | null | null | null | null | null | null |
2,006.07164 | ESAD: Endoscopic Surgeon Action Detection Dataset | ['Vivek Singh Bawa', 'Gurkirt Singh', 'Francis KapingA', 'Inna Skarga-Bandurova', 'Alice Leporini', 'Carmela Landolfo', 'Armando Stabile', 'Francesco Setti', 'Riccardo Muradore', 'Elettra Oleari', 'Fabio Cuzzolin'] | ['cs.CV', 'cs.RO'] | In this work, we take aim towards increasing the effectiveness of surgical
assistant robots. We intended to make assistant robots safer by making them
aware about the actions of surgeon, so it can take appropriate assisting
actions. In other words, we aim to solve the problem of surgeon action
detection in endoscopic v... | 2020-06-12T13:22:41Z | In context of SARAS ESAD Challeneg at MIDL | null | null | ESAD: Endoscopic Surgeon Action Detection Dataset | ['V. Bawa', 'Gurkirt Singh', 'Francis KapingA', 'InnaSkarga-Bandurova', 'A. Leporini', 'Carmela Landolfo', 'Armando Stabile', 'Francesco Setti', 'R. Muradore', 'Elettra Oleari', 'Fabio Cuzzolin'] | 2,020 | arXiv.org | 15 | 30 | ['Computer Science'] |
2,006.07698 | Transferring Monolingual Model to Low-Resource Language: The Case of
Tigrinya | ['Abrhalei Tela', 'Abraham Woubie', 'Ville Hautamaki'] | ['cs.CL', 'cs.LG'] | In recent years, transformer models have achieved great success in natural
language processing (NLP) tasks. Most of the current state-of-the-art NLP
results are achieved by using monolingual transformer models, where the model
is pre-trained using a single language unlabelled text corpus. Then, the model
is fine-tuned ... | 2020-06-13T18:53:22Z | null | null | null | null | null | null | null | null | null | null |
2,006.07733 | Bootstrap your own latent: A new approach to self-supervised Learning | ['Jean-Bastien Grill', 'Florian Strub', 'Florent Altché', 'Corentin Tallec', 'Pierre H. Richemond', 'Elena Buchatskaya', 'Carl Doersch', 'Bernardo Avila Pires', 'Zhaohan Daniel Guo', 'Mohammad Gheshlaghi Azar', 'Bilal Piot', 'Koray Kavukcuoglu', 'Rémi Munos', 'Michal Valko'] | ['cs.LG', 'cs.CV', 'stat.ML'] | We introduce Bootstrap Your Own Latent (BYOL), a new approach to
self-supervised image representation learning. BYOL relies on two neural
networks, referred to as online and target networks, that interact and learn
from each other. From an augmented view of an image, we train the online
network to predict the target ne... | 2020-06-13T22:35:21Z | null | null | null | null | null | null | null | null | null | null |
2,006.0789 | FinEst BERT and CroSloEngual BERT: less is more in multilingual models | ['Matej Ulčar', 'Marko Robnik-Šikonja'] | ['cs.CL'] | Large pretrained masked language models have become state-of-the-art
solutions for many NLP problems. The research has been mostly focused on
English language, though. While massively multilingual models exist, studies
have shown that monolingual models produce much better results. We train two
trilingual BERT-like mod... | 2020-06-14T12:54:01Z | 10 pages, accepted at TSD 2020 conference | Proceedings of the 23rd Internetional Conference on Text, Speech,
and Dialogue (TSD 2020), pages 104-111 | null | null | null | null | null | null | null | null |
2,006.08097 | FinBERT: A Pretrained Language Model for Financial Communications | ['Yi Yang', 'Mark Christopher Siy UY', 'Allen Huang'] | ['cs.CL'] | Contextual pretrained language models, such as BERT (Devlin et al., 2019),
have made significant breakthrough in various NLP tasks by training on large
scale of unlabeled text re-sources.Financial sector also accumulates large
amount of financial communication text.However, there is no pretrained finance
specific langu... | 2020-06-15T02:51:06Z | https://github.com/yya518/FinBERT | null | null | null | null | null | null | null | null | null |
2,006.09092 | Learning Rates as a Function of Batch Size: A Random Matrix Theory
Approach to Neural Network Training | ['Diego Granziol', 'Stefan Zohren', 'Stephen Roberts'] | ['stat.ML', 'cs.LG'] | We study the effect of mini-batching on the loss landscape of deep neural
networks using spiked, field-dependent random matrix theory. We demonstrate
that the magnitude of the extremal values of the batch Hessian are larger than
those of the empirical Hessian. We also derive similar results for the
Generalised Gauss-Ne... | 2020-06-16T11:55:45Z | null | null | null | null | null | null | null | null | null | null |
2,006.09158 | G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided
Glaucoma Detection | ['Muhammad Naseer Bajwa', 'Gur Amrit Pal Singh', 'Wolfgang Neumeier', 'Muhammad Imran Malik', 'Andreas Dengel', 'Sheraz Ahmed'] | ['eess.IV', 'cs.CV', 'cs.LG'] | Scarcity of large publicly available retinal fundus image datasets for
automated glaucoma detection has been the bottleneck for successful application
of artificial intelligence towards practical Computer-Aided Diagnosis (CAD). A
few small datasets that are available for research community usually suffer
from impractic... | 2020-05-28T14:29:03Z | Accepted in IJCNN-2020, 7 pages, 5 figures | null | null | G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detection | ['Muhammad Naseer Bajwa', 'Gurbinder Singh', 'Wolfgang Neumeier', 'M. I. Malik', 'A. Dengel', 'Sheraz Ahmed'] | 2,020 | IEEE International Joint Conference on Neural Network | 80 | 34 | ['Computer Science', 'Engineering'] |
2,006.09882 | Unsupervised Learning of Visual Features by Contrasting Cluster
Assignments | ['Mathilde Caron', 'Ishan Misra', 'Julien Mairal', 'Priya Goyal', 'Piotr Bojanowski', 'Armand Joulin'] | ['cs.CV'] | Unsupervised image representations have significantly reduced the gap with
supervised pretraining, notably with the recent achievements of contrastive
learning methods. These contrastive methods typically work online and rely on a
large number of explicit pairwise feature comparisons, which is computationally
challengi... | 2020-06-17T14:00:42Z | NeurIPS 2020 | null | null | Unsupervised Learning of Visual Features by Contrasting Cluster Assignments | ['Mathilde Caron', 'Ishan Misra', 'J. Mairal', 'Priya Goyal', 'Piotr Bojanowski', 'Armand Joulin'] | 2,020 | Neural Information Processing Systems | 4,115 | 69 | ['Computer Science'] |
2,006.10029 | Big Self-Supervised Models are Strong Semi-Supervised Learners | ['Ting Chen', 'Simon Kornblith', 'Kevin Swersky', 'Mohammad Norouzi', 'Geoffrey Hinton'] | ['cs.LG', 'cs.CV', 'stat.ML'] | One paradigm for learning from few labeled examples while making best use of
a large amount of unlabeled data is unsupervised pretraining followed by
supervised fine-tuning. Although this paradigm uses unlabeled data in a
task-agnostic way, in contrast to common approaches to semi-supervised learning
for computer visio... | 2020-06-17T17:48:22Z | NeurIPS'2020. Code and pretrained models at
https://github.com/google-research/simclr | null | null | Big Self-Supervised Models are Strong Semi-Supervised Learners | ['Ting Chen', 'Simon Kornblith', 'Kevin Swersky', 'Mohammad Norouzi', 'Geoffrey E. Hinton'] | 2,020 | Neural Information Processing Systems | 2,258 | 75 | ['Computer Science', 'Mathematics'] |
2,006.10204 | BlazePose: On-device Real-time Body Pose tracking | ['Valentin Bazarevsky', 'Ivan Grishchenko', 'Karthik Raveendran', 'Tyler Zhu', 'Fan Zhang', 'Matthias Grundmann'] | ['cs.CV'] | We present BlazePose, a lightweight convolutional neural network architecture
for human pose estimation that is tailored for real-time inference on mobile
devices. During inference, the network produces 33 body keypoints for a single
person and runs at over 30 frames per second on a Pixel 2 phone. This makes it
particu... | 2020-06-17T23:52:46Z | 4 pages, 6 figures; CVPR Workshop on Computer Vision for Augmented
and Virtual Reality, Seattle, WA, USA, 2020 | null | null | BlazePose: On-device Real-time Body Pose tracking | ['Valentin Bazarevsky', 'Ivan Grishchenko', 'Karthik Raveendran', 'Tyler Lixuan Zhu', 'Fan Zhang', 'Matthias Grundmann'] | 2,020 | arXiv.org | 592 | 11 | ['Computer Science'] |
2,006.10214 | MediaPipe Hands: On-device Real-time Hand Tracking | ['Fan Zhang', 'Valentin Bazarevsky', 'Andrey Vakunov', 'Andrei Tkachenka', 'George Sung', 'Chuo-Ling Chang', 'Matthias Grundmann'] | ['cs.CV'] | We present a real-time on-device hand tracking pipeline that predicts hand
skeleton from single RGB camera for AR/VR applications. The pipeline consists
of two models: 1) a palm detector, 2) a hand landmark model. It's implemented
via MediaPipe, a framework for building cross-platform ML solutions. The
proposed model a... | 2020-06-18T00:19:13Z | 5 pages, 7 figures; CVPR Workshop on Computer Vision for Augmented
and Virtual Reality, Seattle, WA, USA, 2020 | null | null | null | null | null | null | null | null | null |
2,006.10369 | Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine
Translation | ['Jungo Kasai', 'Nikolaos Pappas', 'Hao Peng', 'James Cross', 'Noah A. Smith'] | ['cs.CL'] | Much recent effort has been invested in non-autoregressive neural machine
translation, which appears to be an efficient alternative to state-of-the-art
autoregressive machine translation on modern GPUs. In contrast to the latter,
where generation is sequential, the former allows generation to be parallelized
across tar... | 2020-06-18T09:06:49Z | ICLR 2021 Final Version | null | null | null | null | null | null | null | null | null |
2,006.10518 | Improving Post Training Neural Quantization: Layer-wise Calibration and
Integer Programming | ['Itay Hubara', 'Yury Nahshan', 'Yair Hanani', 'Ron Banner', 'Daniel Soudry'] | ['cs.LG', 'stat.ML'] | Lately, post-training quantization methods have gained considerable
attention, as they are simple to use, and require only a small unlabeled
calibration set. This small dataset cannot be used to fine-tune the model
without significant over-fitting. Instead, these methods only use the
calibration set to set the activati... | 2020-06-14T16:07:55Z | null | null | null | Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming | ['Itay Hubara', 'Yury Nahshan', 'Yair Hanani', 'Ron Banner', 'Daniel Soudry'] | 2,020 | arXiv.org | 129 | 27 | ['Computer Science', 'Mathematics'] |
2,006.10802 | DS6, Deformation-aware Semi-supervised Learning: Application to Small
Vessel Segmentation with Noisy Training Data | ['Soumick Chatterjee', 'Kartik Prabhu', 'Mahantesh Pattadkal', 'Gerda Bortsova', 'Chompunuch Sarasaen', 'Florian Dubost', 'Hendrik Mattern', 'Marleen de Bruijne', 'Oliver Speck', 'Andreas Nürnberger'] | ['eess.IV', 'cs.CV', 'cs.LG', '68T07 (Primary) 68T45 (Secondary)', 'I.2.6; I.4.6'] | Blood vessels of the brain provide the human brain with the required
nutrients and oxygen. As a vulnerable part of the cerebral blood supply,
pathology of small vessels can cause serious problems such as Cerebral Small
Vessel Diseases (CSVD). It has also been shown that CSVD is related to
neurodegeneration, such as Alz... | 2020-06-18T18:42:57Z | null | Journal of Imaging. 2022; 8(10):259 | 10.3390/jimaging8100259 | null | null | null | null | null | null | null |
2,006.10962 | Attention Mesh: High-fidelity Face Mesh Prediction in Real-time | ['Ivan Grishchenko', 'Artsiom Ablavatski', 'Yury Kartynnik', 'Karthik Raveendran', 'Matthias Grundmann'] | ['cs.CV'] | We present Attention Mesh, a lightweight architecture for 3D face mesh
prediction that uses attention to semantically meaningful regions. Our neural
network is designed for real-time on-device inference and runs at over 50 FPS
on a Pixel 2 phone. Our solution enables applications like AR makeup, eye
tracking and AR pup... | 2020-06-19T05:07:38Z | 4 pages, 5 figures; CVPR Workshop on Computer Vision for Augmented
and Virtual Reality, Seattle, WA, USA, 2020 | null | null | null | null | null | null | null | null | null |
2,006.11063 | Dataset for Automatic Summarization of Russian News | ['Ilya Gusev'] | ['cs.CL'] | Automatic text summarization has been studied in a variety of domains and
languages. However, this does not hold for the Russian language. To overcome
this issue, we present Gazeta, the first dataset for summarization of Russian
news. We describe the properties of this dataset and benchmark several
extractive and abstr... | 2020-06-19T10:44:06Z | Version 4, October 2021, corrected BLEU scores | In: AINL 2020. Communications in Computer and Information Science,
vol 1292. Springer, Cham (2020) | 10.1007/978-3-030-59082-6_9 | null | null | null | null | null | null | null |
2,006.11239 | Denoising Diffusion Probabilistic Models | ['Jonathan Ho', 'Ajay Jain', 'Pieter Abbeel'] | ['cs.LG', 'stat.ML'] | We present high quality image synthesis results using diffusion probabilistic
models, a class of latent variable models inspired by considerations from
nonequilibrium thermodynamics. Our best results are obtained by training on a
weighted variational bound designed according to a novel connection between
diffusion prob... | 2020-06-19T17:24:44Z | null | null | null | Denoising Diffusion Probabilistic Models | ['Jonathan Ho', 'Ajay Jain', 'P. Abbeel'] | 2,020 | Neural Information Processing Systems | 18,550 | 73 | ['Computer Science', 'Mathematics'] |
2,006.11316 | SqueezeBERT: What can computer vision teach NLP about efficient neural
networks? | ['Forrest N. Iandola', 'Albert E. Shaw', 'Ravi Krishna', 'Kurt W. Keutzer'] | ['cs.CL', 'cs.CV', 'cs.LG'] | Humans read and write hundreds of billions of messages every day. Further,
due to the availability of large datasets, large computing systems, and better
neural network models, natural language processing (NLP) technology has made
significant strides in understanding, proofreading, and organizing these
messages. Thus, ... | 2020-06-19T18:40:29Z | 9 pages + appendix | null | null | null | null | null | null | null | null | null |
2,006.11477 | wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations | ['Alexei Baevski', 'Henry Zhou', 'Abdelrahman Mohamed', 'Michael Auli'] | ['cs.CL', 'cs.LG', 'cs.SD', 'eess.AS'] | We show for the first time that learning powerful representations from speech
audio alone followed by fine-tuning on transcribed speech can outperform the
best semi-supervised methods while being conceptually simpler. wav2vec 2.0
masks the speech input in the latent space and solves a contrastive task
defined over a qu... | 2020-06-20T02:35:02Z | null | null | null | wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations | ['Alexei Baevski', 'Henry Zhou', 'Abdel-rahman Mohamed', 'Michael Auli'] | 2,020 | Neural Information Processing Systems | 5,880 | 61 | ['Computer Science', 'Engineering'] |
2,006.13979 | Unsupervised Cross-lingual Representation Learning for Speech
Recognition | ['Alexis Conneau', 'Alexei Baevski', 'Ronan Collobert', 'Abdelrahman Mohamed', 'Michael Auli'] | ['cs.CL', 'cs.LG', 'cs.SD', 'eess.AS'] | This paper presents XLSR which learns cross-lingual speech representations by
pretraining a single model from the raw waveform of speech in multiple
languages. We build on wav2vec 2.0 which is trained by solving a contrastive
task over masked latent speech representations and jointly learns a
quantization of the latent... | 2020-06-24T18:25:05Z | null | null | null | null | null | null | null | null | null | null |
2,006.1409 | Neural Architecture Design for GPU-Efficient Networks | ['Ming Lin', 'Hesen Chen', 'Xiuyu Sun', 'Qi Qian', 'Hao Li', 'Rong Jin'] | ['cs.CV'] | Many mission-critical systems are based on GPU for inference. It requires not
only high recognition accuracy but also low latency in responding time.
Although many studies are devoted to optimizing the structure of deep models
for efficient inference, most of them do not leverage the architecture of
\textbf{modern GPU}... | 2020-06-24T22:42:18Z | update training setting | null | null | null | null | null | null | null | null | null |
2,006.14147 | FastSpec: Scalable Generation and Detection of Spectre Gadgets Using
Neural Embeddings | ['M. Caner Tol', 'Berk Gulmezoglu', 'Koray Yurtseven', 'Berk Sunar'] | ['cs.CR', 'cs.LG'] | Several techniques have been proposed to detect vulnerable Spectre gadgets in
widely deployed commercial software. Unfortunately, detection techniques
proposed so far rely on hand-written rules which fall short in covering subtle
variations of known Spectre gadgets as well as demand a huge amount of time to
analyze eac... | 2020-06-25T03:08:20Z | IEEE European Symposium on Security and Privacy 2021 | null | null | FastSpec: Scalable Generation and Detection of Spectre Gadgets Using Neural Embeddings | ['M. Caner Tol', 'Koray Yurtseven', 'Berk Gülmezoglu', 'B. Sunar'] | 2,020 | European Symposium on Security and Privacy | 16 | 82 | ['Computer Science'] |
2,006.15418 | Counting Out Time: Class Agnostic Video Repetition Counting in the Wild | ['Debidatta Dwibedi', 'Yusuf Aytar', 'Jonathan Tompson', 'Pierre Sermanet', 'Andrew Zisserman'] | ['cs.CV'] | We present an approach for estimating the period with which an action is
repeated in a video. The crux of the approach lies in constraining the period
prediction module to use temporal self-similarity as an intermediate
representation bottleneck that allows generalization to unseen repetitions in
videos in the wild. We... | 2020-06-27T18:00:42Z | Accepted at CVPR 2020. Project webpage:
https://sites.google.com/view/repnet | null | null | Counting Out Time: Class Agnostic Video Repetition Counting in the Wild | ['Debidatta Dwibedi', 'Y. Aytar', 'Jonathan Tompson', 'P. Sermanet', 'Andrew Zisserman'] | 2,020 | Computer Vision and Pattern Recognition | 114 | 56 | ['Computer Science'] |
2,006.15994 | Improving Sequence Tagging for Vietnamese Text Using Transformer-based
Neural Models | ['Viet Bui The', 'Oanh Tran Thi', 'Phuong Le-Hong'] | ['cs.CL'] | This paper describes our study on using mutilingual BERT embeddings and some
new neural models for improving sequence tagging tasks for the Vietnamese
language. We propose new model architectures and evaluate them extensively on
two named entity recognition datasets of VLSP 2016 and VLSP 2018, and on two
part-of-speech... | 2020-06-29T12:39:44Z | Accepted at the Conference PACLIC 2020 | null | null | Improving Sequence Tagging for Vietnamese Text using Transformer-based Neural Models | ['Viet The Bui', 'Oanh T. K. Tran', 'Hong Phuong Le'] | 2,020 | Pacific Asia Conference on Language, Information and Computation | 40 | 27 | ['Computer Science'] |
2,006.16668 | GShard: Scaling Giant Models with Conditional Computation and Automatic
Sharding | ['Dmitry Lepikhin', 'HyoukJoong Lee', 'Yuanzhong Xu', 'Dehao Chen', 'Orhan Firat', 'Yanping Huang', 'Maxim Krikun', 'Noam Shazeer', 'Zhifeng Chen'] | ['cs.CL', 'cs.LG', 'stat.ML'] | Neural network scaling has been critical for improving the model quality in
many real-world machine learning applications with vast amounts of training
data and compute. Although this trend of scaling is affirmed to be a sure-fire
approach for better model quality, there are challenges on the path such as the
computati... | 2020-06-30T10:42:02Z | null | null | null | null | null | null | null | null | null | null |
2,007.00224 | Debiased Contrastive Learning | ['Ching-Yao Chuang', 'Joshua Robinson', 'Lin Yen-Chen', 'Antonio Torralba', 'Stefanie Jegelka'] | ['cs.LG', 'stat.ML'] | A prominent technique for self-supervised representation learning has been to
contrast semantically similar and dissimilar pairs of samples. Without access
to labels, dissimilar (negative) points are typically taken to be randomly
sampled datapoints, implicitly accepting that these points may, in reality,
actually have... | 2020-07-01T04:25:24Z | null | Advances in Neural Information Processing Systems (2020) | null | null | null | null | null | null | null | null |
2,007.00398 | DocVQA: A Dataset for VQA on Document Images | ['Minesh Mathew', 'Dimosthenis Karatzas', 'C. V. Jawahar'] | ['cs.CV', 'cs.IR'] | We present a new dataset for Visual Question Answering (VQA) on document
images called DocVQA. The dataset consists of 50,000 questions defined on
12,000+ document images. Detailed analysis of the dataset in comparison with
similar datasets for VQA and reading comprehension is presented. We report
several baseline resu... | 2020-07-01T11:37:40Z | accepted at WACV 2021 | null | null | null | null | null | null | null | null | null |
2,007.00808 | Approximate Nearest Neighbor Negative Contrastive Learning for Dense
Text Retrieval | ['Lee Xiong', 'Chenyan Xiong', 'Ye Li', 'Kwok-Fung Tang', 'Jialin Liu', 'Paul Bennett', 'Junaid Ahmed', 'Arnold Overwijk'] | ['cs.IR', 'cs.CL', 'cs.LG'] | Conducting text retrieval in a dense learned representation space has many
intriguing advantages over sparse retrieval. Yet the effectiveness of dense
retrieval (DR) often requires combination with sparse retrieval. In this paper,
we identify that the main bottleneck is in the training mechanisms, where the
negative in... | 2020-07-01T23:15:56Z | null | null | null | null | null | null | null | null | null | null |
2,007.00814 | Relevance-guided Supervision for OpenQA with ColBERT | ['Omar Khattab', 'Christopher Potts', 'Matei Zaharia'] | ['cs.CL', 'cs.IR'] | Systems for Open-Domain Question Answering (OpenQA) generally depend on a
retriever for finding candidate passages in a large corpus and a reader for
extracting answers from those passages. In much recent work, the retriever is a
learned component that uses coarse-grained vector representations of questions
and passage... | 2020-07-01T23:50:58Z | Accepted for publication in Transactions of the Association for
Computational Linguistics (TACL), 2021. Author's final version. Oral
presentation at ACL'21 | null | null | Relevance-guided Supervision for OpenQA with ColBERT | ['O. Khattab', 'Christopher Potts', 'M. Zaharia'] | 2,020 | Transactions of the Association for Computational Linguistics | 100 | 46 | ['Computer Science'] |
2,007.00992 | Rethinking Channel Dimensions for Efficient Model Design | ['Dongyoon Han', 'Sangdoo Yun', 'Byeongho Heo', 'YoungJoon Yoo'] | ['cs.CV'] | Designing an efficient model within the limited computational cost is
challenging. We argue the accuracy of a lightweight model has been further
limited by the design convention: a stage-wise configuration of the channel
dimensions, which looks like a piecewise linear function of the network stage.
In this paper, we st... | 2020-07-02T10:01:12Z | 13 pages, 8 figures, CVPR 2021 | null | null | Rethinking Channel Dimensions for Efficient Model Design | ['Dongyoon Han', 'Sangdoo Yun', 'Byeongho Heo', 'Y. Yoo'] | 2,020 | Computer Vision and Pattern Recognition | 86 | 66 | ['Computer Science'] |
2,007.01282 | Leveraging Passage Retrieval with Generative Models for Open Domain
Question Answering | ['Gautier Izacard', 'Edouard Grave'] | ['cs.CL', 'cs.LG'] | Generative models for open domain question answering have proven to be
competitive, without resorting to external knowledge. While promising, this
approach requires to use models with billions of parameters, which are
expensive to train and query. In this paper, we investigate how much these
models can benefit from ret... | 2020-07-02T17:44:57Z | null | null | null | null | null | null | null | null | null | null |
2,007.01658 | Playing with Words at the National Library of Sweden -- Making a Swedish
BERT | ['Martin Malmsten', 'Love Börjeson', 'Chris Haffenden'] | ['cs.CL'] | This paper introduces the Swedish BERT ("KB-BERT") developed by the KBLab for
data-driven research at the National Library of Sweden (KB). Building on recent
efforts to create transformer-based BERT models for languages other than
English, we explain how we used KB's collections to create and train a new
language-speci... | 2020-07-03T12:53:39Z | null | null | null | Playing with Words at the National Library of Sweden - Making a Swedish BERT | ['Martin Malmsten', 'Love Börjeson', 'Chris Haffenden'] | 2,020 | arXiv.org | 126 | 18 | ['Computer Science'] |
2,007.01852 | Language-agnostic BERT Sentence Embedding | ['Fangxiaoyu Feng', 'Yinfei Yang', 'Daniel Cer', 'Naveen Arivazhagan', 'Wei Wang'] | ['cs.CL'] | While BERT is an effective method for learning monolingual sentence
embeddings for semantic similarity and embedding based transfer learning
(Reimers and Gurevych, 2019), BERT based cross-lingual sentence embeddings have
yet to be explored. We systematically investigate methods for learning
multilingual sentence embedd... | 2020-07-03T17:58:42Z | To be presented at ACL 2022 | null | null | Language-agnostic BERT Sentence Embedding | ['Fangxiaoyu Feng', 'Yinfei Yang', 'Daniel Matthew Cer', 'N. Arivazhagan', 'Wei Wang'] | 2,020 | Annual Meeting of the Association for Computational Linguistics | 921 | 51 | ['Computer Science'] |
2,007.02713 | Bifurcated backbone strategy for RGB-D salient object detection | ['Yingjie Zhai', 'Deng-Ping Fan', 'Jufeng Yang', 'Ali Borji', 'Ling Shao', 'Junwei Han', 'Liang Wang'] | ['cs.CV'] | Multi-level feature fusion is a fundamental topic in computer vision. It has
been exploited to detect, segment and classify objects at various scales. When
multi-level features meet multi-modal cues, the optimal feature aggregation and
multi-modal learning strategy become a hot potato. In this paper, we leverage
the in... | 2020-07-06T13:01:30Z | A preliminary version of this work has been accepted in ECCV 2020 | IEEE Transactions on Image Processing, 2021, 30: 8727-8742 | 10.1109/TIP.2021.3116793 | null | null | null | null | null | null | null |
2,007.05194 | What Can We Learn From Almost a Decade of Food Tweets | ['Uga Sproģis', 'Matīss Rikters'] | ['cs.CL'] | We present the Latvian Twitter Eater Corpus - a set of tweets in the narrow
domain related to food, drinks, eating and drinking. The corpus has been
collected over time-span of over 8 years and includes over 2 million tweets
entailed with additional useful data. We also separate two sub-corpora of
question and answer t... | 2020-07-10T06:36:13Z | null | In Proceedings of the 9th Conference Human Language Technologies -
The Baltic Perspective (Baltic HLT 2020) | null | null | null | null | null | null | null | null |
2,007.05612 | Multi-Dialect Arabic BERT for Country-Level Dialect Identification | ['Bashar Talafha', 'Mohammad Ali', "Muhy Eddin Za'ter", 'Haitham Seelawi', 'Ibraheem Tuffaha', 'Mostafa Samir', 'Wael Farhan', 'Hussein T. Al-Natsheh'] | ['cs.CL', 'cs.LG'] | Arabic dialect identification is a complex problem for a number of inherent
properties of the language itself. In this paper, we present the experiments
conducted, and the models developed by our competing team, Mawdoo3 AI, along
the way to achieving our winning solution to subtask 1 of the Nuanced Arabic
Dialect Ident... | 2020-07-10T21:11:46Z | Accepted at the Fifth Arabic Natural Language Processing Workshop
(WANLP2020) co-located with the 28th International Conference on
Computational Linguistics (COLING'2020), Barcelona, Spain, 12 Dec. 2020 | null | null | Multi-dialect Arabic BERT for Country-level Dialect Identification | ['Bashar Talafha', 'Mohammad Ali', "Muhy Eddin Za'ter", 'Haitham Seelawi', 'Ibraheem Tuffaha', 'Mostafa Samir', 'Wael Farhan', 'Hussein T. Al-Natsheh'] | 2,020 | Workshop on Arabic Natural Language Processing | 53 | 30 | ['Computer Science'] |
2,007.06346 | Whitening for Self-Supervised Representation Learning | ['Aleksandr Ermolov', 'Aliaksandr Siarohin', 'Enver Sangineto', 'Nicu Sebe'] | ['cs.LG', 'cs.CV', 'stat.ML'] | Most of the current self-supervised representation learning (SSL) methods are
based on the contrastive loss and the instance-discrimination task, where
augmented versions of the same image instance ("positives") are contrasted with
instances extracted from other images ("negatives"). For the learning to be
effective, m... | 2020-07-13T12:33:25Z | ICML 2021 | null | null | Whitening for Self-Supervised Representation Learning | ['Aleksandr Ermolov', 'Aliaksandr Siarohin', 'E. Sangineto', 'N. Sebe'] | 2,020 | International Conference on Machine Learning | 316 | 65 | ['Computer Science', 'Mathematics'] |
2,007.07779 | AdapterHub: A Framework for Adapting Transformers | ['Jonas Pfeiffer', 'Andreas Rücklé', 'Clifton Poth', 'Aishwarya Kamath', 'Ivan Vulić', 'Sebastian Ruder', 'Kyunghyun Cho', 'Iryna Gurevych'] | ['cs.CL'] | The current modus operandi in NLP involves downloading and fine-tuning
pre-trained models consisting of millions or billions of parameters. Storing
and sharing such large trained models is expensive, slow, and time-consuming,
which impedes progress towards more general and versatile NLP methods that
learn from and for ... | 2020-07-15T15:56:05Z | EMNLP 2020: Systems Demonstrations | null | null | null | null | null | null | null | null | null |
2,007.07834 | InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language
Model Pre-Training | ['Zewen Chi', 'Li Dong', 'Furu Wei', 'Nan Yang', 'Saksham Singhal', 'Wenhui Wang', 'Xia Song', 'Xian-Ling Mao', 'Heyan Huang', 'Ming Zhou'] | ['cs.CL'] | In this work, we present an information-theoretic framework that formulates
cross-lingual language model pre-training as maximizing mutual information
between multilingual-multi-granularity texts. The unified view helps us to
better understand the existing methods for learning cross-lingual
representations. More import... | 2020-07-15T16:58:01Z | NAACL 2021 | null | null | null | null | null | null | null | null | null |
2,007.08489 | Do Adversarially Robust ImageNet Models Transfer Better? | ['Hadi Salman', 'Andrew Ilyas', 'Logan Engstrom', 'Ashish Kapoor', 'Aleksander Madry'] | ['cs.CV', 'cs.LG', 'stat.ML'] | Transfer learning is a widely-used paradigm in deep learning, where models
pre-trained on standard datasets can be efficiently adapted to downstream
tasks. Typically, better pre-trained models yield better transfer results,
suggesting that initial accuracy is a key aspect of transfer learning
performance. In this work,... | 2020-07-16T17:42:40Z | NeurIPS 2020 | null | null | Do Adversarially Robust ImageNet Models Transfer Better? | ['Hadi Salman', 'Andrew Ilyas', 'Logan Engstrom', 'Ashish Kapoor', 'A. Ma̧dry'] | 2,020 | Neural Information Processing Systems | 428 | 103 | ['Computer Science', 'Mathematics'] |
2,007.09127 | CTC-Segmentation of Large Corpora for German End-to-end Speech
Recognition | ['Ludwig Kürzinger', 'Dominik Winkelbauer', 'Lujun Li', 'Tobias Watzel', 'Gerhard Rigoll'] | ['eess.AS'] | Recent end-to-end Automatic Speech Recognition (ASR) systems demonstrated the
ability to outperform conventional hybrid DNN/ HMM ASR. Aside from
architectural improvements in those systems, those models grew in terms of
depth, parameters and model capacity. However, these models also require more
training data to achie... | 2020-07-17T17:38:08Z | Published at SPECOM 2020 | Speech and Computer (2020) | 10.1007/978-3-030-60276-5_27 | null | null | null | null | null | null | null |
2,007.14062 | Big Bird: Transformers for Longer Sequences | ['Manzil Zaheer', 'Guru Guruganesh', 'Avinava Dubey', 'Joshua Ainslie', 'Chris Alberti', 'Santiago Ontanon', 'Philip Pham', 'Anirudh Ravula', 'Qifan Wang', 'Li Yang', 'Amr Ahmed'] | ['cs.LG', 'cs.CL', 'stat.ML'] | Transformers-based models, such as BERT, have been one of the most successful
deep learning models for NLP. Unfortunately, one of their core limitations is
the quadratic dependency (mainly in terms of memory) on the sequence length due
to their full attention mechanism. To remedy this, we propose, BigBird, a
sparse att... | 2020-07-28T08:34:04Z | null | Neural Information Processing Systems (NeurIPS) 2020 | null | Big Bird: Transformers for Longer Sequences | ['M. Zaheer', 'Guru Guruganesh', 'Kumar Avinava Dubey', 'J. Ainslie', 'Chris Alberti', 'Santiago Ontañón', 'Philip Pham', 'Anirudh Ravula', 'Qifan Wang', 'Li Yang', 'Amr Ahmed'] | 2,020 | Neural Information Processing Systems | 2,111 | 118 | ['Computer Science', 'Mathematics', 'Geography'] |
2,007.14271 | Declarative Experimentation in Information Retrieval using PyTerrier | ['Craig Macdonald', 'Nicola Tonellotto'] | ['cs.IR'] | The advent of deep machine learning platforms such as Tensorflow and Pytorch,
developed in expressive high-level languages such as Python, have allowed more
expressive representations of deep neural network architectures. We argue that
such a powerful formalism is missing in information retrieval (IR), and propose
a fr... | 2020-07-28T14:36:29Z | null | 2020 ACM SIGIR International Conference on the Theory of
Information Retrieval (ICTIR '20) | 10.1145/3409256.3409829 | Declarative Experimentation in Information Retrieval using PyTerrier | ['Craig Macdonald', 'N. Tonellotto'] | 2,020 | International Conference on the Theory of Information Retrieval | 147 | 30 | ['Computer Science'] |
2,007.14937 | Learning Video Representations from Textual Web Supervision | ['Jonathan C. Stroud', 'Zhichao Lu', 'Chen Sun', 'Jia Deng', 'Rahul Sukthankar', 'Cordelia Schmid', 'David A. Ross'] | ['cs.CV'] | Videos on the Internet are paired with pieces of text, such as titles and
descriptions. This text typically describes the most important content in the
video, such as the objects in the scene and the actions being performed. Based
on this observation, we propose to use text as a method for learning video
representation... | 2020-07-29T16:19:50Z | null | null | null | null | null | null | null | null | null | null |
2,007.14966 | Mirostat: A Neural Text Decoding Algorithm that Directly Controls
Perplexity | ['Sourya Basu', 'Govardana Sachitanandam Ramachandran', 'Nitish Shirish Keskar', 'Lav R. Varshney'] | ['cs.CL', 'cs.IT', 'math.IT'] | Neural text decoding is important for generating high-quality texts using
language models. To generate high-quality text, popular decoding algorithms
like top-k, top-p (nucleus), and temperature-based sampling truncate or distort
the unreliable low probability tail of the language model. Though these methods
generate h... | 2020-07-29T17:22:26Z | 25 pages, 12 figures | null | null | null | null | null | null | null | null | null |
2,007.15207 | MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain
Question Answering | ['Shayne Longpre', 'Yi Lu', 'Joachim Daiber'] | ['cs.CL'] | Progress in cross-lingual modeling depends on challenging, realistic, and
diverse evaluation sets. We introduce Multilingual Knowledge Questions and
Answers (MKQA), an open-domain question answering evaluation set comprising 10k
question-answer pairs aligned across 26 typologically diverse languages (260k
question-answ... | 2020-07-30T03:33:46Z | null | null | null | null | null | null | null | null | null | null |
2,007.15651 | Contrastive Learning for Unpaired Image-to-Image Translation | ['Taesung Park', 'Alexei A. Efros', 'Richard Zhang', 'Jun-Yan Zhu'] | ['cs.CV', 'cs.LG'] | In image-to-image translation, each patch in the output should reflect the
content of the corresponding patch in the input, independent of domain. We
propose a straightforward method for doing so -- maximizing mutual information
between the two, using a framework based on contrastive learning. The method
encourages two... | 2020-07-30T17:59:58Z | ECCV 2020. Please visit
https://taesungp.github.io/ContrastiveUnpairedTranslation/ for introduction
videos and more. v3 contains typo fixes and citation update | null | null | null | null | null | null | null | null | null |
2,007.15779 | Domain-Specific Language Model Pretraining for Biomedical Natural
Language Processing | ['Yu Gu', 'Robert Tinn', 'Hao Cheng', 'Michael Lucas', 'Naoto Usuyama', 'Xiaodong Liu', 'Tristan Naumann', 'Jianfeng Gao', 'Hoifung Poon'] | ['cs.CL', 'cs.LG'] | Pretraining large neural language models, such as BERT, has led to impressive
gains on many natural language processing (NLP) tasks. However, most
pretraining efforts focus on general domain corpora, such as newswire and Web.
A prevailing assumption is that even domain-specific pretraining can benefit by
starting from ... | 2020-07-31T00:04:15Z | ACM Transactions on Computing for Healthcare (HEALTH) | null | 10.1145/3458754 | null | null | null | null | null | null | null |
2,008.00401 | Multilingual Translation with Extensible Multilingual Pretraining and
Finetuning | ['Yuqing Tang', 'Chau Tran', 'Xian Li', 'Peng-Jen Chen', 'Naman Goyal', 'Vishrav Chaudhary', 'Jiatao Gu', 'Angela Fan'] | ['cs.CL'] | Recent work demonstrates the potential of multilingual pretraining of
creating one model that can be used for various tasks in different languages.
Previous work in multilingual pretraining has demonstrated that machine
translation systems can be created by finetuning on bitext. In this work, we
show that multilingual ... | 2020-08-02T05:36:55Z | 10 pages (main) + 5 pages (appendices). 9 tables and 2 figures | null | null | null | null | null | null | null | null | null |
2,008.02275 | Aligning AI With Shared Human Values | ['Dan Hendrycks', 'Collin Burns', 'Steven Basart', 'Andrew Critch', 'Jerry Li', 'Dawn Song', 'Jacob Steinhardt'] | ['cs.CY', 'cs.AI', 'cs.CL', 'cs.LG'] | We show how to assess a language model's knowledge of basic concepts of
morality. We introduce the ETHICS dataset, a new benchmark that spans concepts
in justice, well-being, duties, virtues, and commonsense morality. Models
predict widespread moral judgments about diverse text scenarios. This requires
connecting physi... | 2020-08-05T17:59:16Z | ICLR 2021; the ETHICS dataset is available at
https://github.com/hendrycks/ethics/ | null | null | null | null | null | null | null | null | null |
2,008.02496 | ConvBERT: Improving BERT with Span-based Dynamic Convolution | ['Zihang Jiang', 'Weihao Yu', 'Daquan Zhou', 'Yunpeng Chen', 'Jiashi Feng', 'Shuicheng Yan'] | ['cs.CL'] | Pre-trained language models like BERT and its variants have recently achieved
impressive performance in various natural language understanding tasks.
However, BERT heavily relies on the global self-attention block and thus
suffers large memory footprint and computation cost. Although all its attention
heads query on th... | 2020-08-06T07:43:19Z | 17 pages | null | null | ConvBERT: Improving BERT with Span-based Dynamic Convolution | ['Zihang Jiang', 'Weihao Yu', 'Daquan Zhou', 'Yunpeng Chen', 'Jiashi Feng', 'Shuicheng Yan'] | 2,020 | Neural Information Processing Systems | 163 | 81 | ['Computer Science'] |
2,008.03415 | Assessing Demographic Bias in Named Entity Recognition | ['Shubhanshu Mishra', 'Sijun He', 'Luca Belli'] | ['cs.CL', 'cs.CY', 'cs.IR', 'cs.LG', '68T50 (Primary), 68T30 (Secondary), 68U15', 'I.2.7; I.2.1; I.2.6; H.3.1; H.3.3; H.1.2; K.4.2'] | Named Entity Recognition (NER) is often the first step towards automated
Knowledge Base (KB) generation from raw text. In this work, we assess the bias
in various Named Entity Recognition (NER) systems for English across different
demographic groups with synthetically generated corpora. Our analysis reveals
that models... | 2020-08-08T02:01:25Z | Presented at the AKBC Workshop on Bias in Automatic Knowledge Graph
Construction, 2020 (arXiv:2007.11659) | null | null | Assessing Demographic Bias in Named Entity Recognition | ['Shubhanshu Mishra', 'Sijun He', 'Luca Belli'] | 2,020 | arXiv.org | 47 | 34 | ['Computer Science'] |
2,008.03802 | SpeedySpeech: Efficient Neural Speech Synthesis | ['Jan Vainer', 'Ondřej Dušek'] | ['eess.AS', 'cs.CL', 'cs.LG', 'cs.SD'] | While recent neural sequence-to-sequence models have greatly improved the
quality of speech synthesis, there has not been a system capable of fast
training, fast inference and high-quality audio synthesis at the same time. We
propose a student-teacher network capable of high-quality faster-than-real-time
spectrogram sy... | 2020-08-09T20:00:57Z | 5 pages, 3 figures, Interspeech 2020 | null | null | null | null | null | null | null | null | null |
2,008.03946 | A Large-Scale Chinese Short-Text Conversation Dataset | ['Yida Wang', 'Pei Ke', 'Yinhe Zheng', 'Kaili Huang', 'Yong Jiang', 'Xiaoyan Zhu', 'Minlie Huang'] | ['cs.CL'] | The advancements of neural dialogue generation models show promising results
on modeling short-text conversations. However, training such models usually
needs a large-scale high-quality dialogue corpus, which is hard to access. In
this paper, we present a large-scale cleaned Chinese conversation dataset,
LCCC, which co... | 2020-08-10T08:12:49Z | Accepted by NLPCC 2020 (Best Student Paper) | null | null | null | null | null | null | null | null | null |
2,008.03979 | KR-BERT: A Small-Scale Korean-Specific Language Model | ['Sangah Lee', 'Hansol Jang', 'Yunmee Baik', 'Suzi Park', 'Hyopil Shin'] | ['cs.CL'] | Since the appearance of BERT, recent works including XLNet and RoBERTa
utilize sentence embedding models pre-trained by large corpora and a large
number of parameters. Because such models have large hardware and a huge amount
of data, they take a long time to pre-train. Therefore it is important to
attempt to make smal... | 2020-08-10T09:26:00Z | 7 pages | null | null | KR-BERT: A Small-Scale Korean-Specific Language Model | ['Sangah Lee', 'Hansol Jang', 'Yunmee Baik', 'Suzi Park', 'Hyopil Shin'] | 2,020 | arXiv.org | 52 | 19 | ['Computer Science'] |
2,008.04162 | Navigating Human Language Models with Synthetic Agents | ['Philip Feldman', 'Antonio Bucchiarone'] | ['cs.AI', 'cs.CL', 'cs.MA', 'I.2; I.6; J.4'] | Modern natural language models such as the GPT-2/GPT-3 contain tremendous
amounts of information about human belief in a consistently testable form. If
these models could be shown to accurately reflect the underlying beliefs of the
human beings that produced the data used to train these models, then such
models become ... | 2020-08-10T14:39:53Z | 8 pages, 6 figures, 2 tables, 1 algorithm | null | null | Navigating Language Models with Synthetic Agents | ['Philip G. Feldman'] | 2,020 | arXiv.org | 4 | 24 | ['Computer Science'] |
2,008.05671 | Large-scale Transfer Learning for Low-resource Spoken Language
Understanding | ['Xueli Jia', 'Jianzong Wang', 'Zhiyong Zhang', 'Ning Cheng', 'Jing Xiao'] | ['eess.AS', 'cs.CL', 'cs.SD'] | End-to-end Spoken Language Understanding (SLU) models are made increasingly
large and complex to achieve the state-ofthe-art accuracy. However, the
increased complexity of a model can also introduce high risk of over-fitting,
which is a major challenge in SLU tasks due to the limitation of available
data. In this paper... | 2020-08-13T03:43:05Z | will be presented in INTERSPEECH 2020 | null | null | null | null | null | null | null | null | null |
2,008.06048 | MMM : Exploring Conditional Multi-Track Music Generation with the
Transformer | ['Jeff Ens', 'Philippe Pasquier'] | ['cs.SD', 'cs.LG', 'cs.MM'] | We propose the Multi-Track Music Machine (MMM), a generative system based on
the Transformer architecture that is capable of generating multi-track music.
In contrast to previous work, which represents musical material as a single
time-ordered sequence, where the musical events corresponding to different
tracks are int... | 2020-08-13T02:36:34Z | null | null | null | null | null | null | null | null | null | null |
2,008.07905 | Glancing Transformer for Non-Autoregressive Neural Machine Translation | ['Lihua Qian', 'Hao Zhou', 'Yu Bao', 'Mingxuan Wang', 'Lin Qiu', 'Weinan Zhang', 'Yong Yu', 'Lei Li'] | ['cs.CL'] | Recent work on non-autoregressive neural machine translation (NAT) aims at
improving the efficiency by parallel decoding without sacrificing the quality.
However, existing NAT methods are either inferior to Transformer or require
multiple decoding passes, leading to reduced speedup. We propose the Glancing
Language Mod... | 2020-08-18T13:04:03Z | 9 pages, 7 figures, ACL2021 | null | null | null | null | null | null | null | null | null |
2,008.08767 | Single Image Super-Resolution via a Holistic Attention Network | ['Ben Niu', 'Weilei Wen', 'Wenqi Ren', 'Xiangde Zhang', 'Lianping Yang', 'Shuzhen Wang', 'Kaihao Zhang', 'Xiaochun Cao', 'Haifeng Shen'] | ['eess.IV', 'cs.CV'] | Informative features play a crucial role in the single image super-resolution
task. Channel attention has been demonstrated to be effective for preserving
information-rich features in each layer. However, channel attention treats each
convolution layer as a separate process that misses the correlation among
different l... | 2020-08-20T04:13:15Z | 16 pages, 6 figures, IEEE International Conference on Computer Vision | null | null | null | null | null | null | null | null | null |
2,008.09093 | PARADE: Passage Representation Aggregation for Document Reranking | ['Canjia Li', 'Andrew Yates', 'Sean MacAvaney', 'Ben He', 'Yingfei Sun'] | ['cs.IR'] | Pretrained transformer models, such as BERT and T5, have shown to be highly
effective at ad-hoc passage and document ranking. Due to inherent sequence
length limits of these models, they need to be run over a document's passages,
rather than processing the entire document sequence at once. Although several
approaches f... | 2020-08-20T17:32:30Z | null | null | null | null | null | null | null | null | null | null |
2,008.09144 | PTT5: Pretraining and validating the T5 model on Brazilian Portuguese
data | ['Diedre Carmo', 'Marcos Piau', 'Israel Campiotti', 'Rodrigo Nogueira', 'Roberto Lotufo'] | ['cs.CL'] | In natural language processing (NLP), there is a need for more resources in
Portuguese, since much of the data used in the state-of-the-art research is in
other languages. In this paper, we pretrain a T5 model on the BrWac corpus, an
extensive collection of web pages in Portuguese, and evaluate its performance
against ... | 2020-08-20T18:10:13Z | null | null | null | PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data | ['D. Carmo', 'Marcos Piau', 'Israel Campiotti', 'Rodrigo Nogueira', 'R. Lotufo'] | 2,020 | arXiv.org | 52 | 28 | ['Computer Science'] |
2,008.1001 | A Lip Sync Expert Is All You Need for Speech to Lip Generation In The
Wild | ['K R Prajwal', 'Rudrabha Mukhopadhyay', 'Vinay Namboodiri', 'C V Jawahar'] | ['cs.CV', 'cs.LG', 'cs.SD', 'eess.AS'] | In this work, we investigate the problem of lip-syncing a talking face video
of an arbitrary identity to match a target speech segment. Current works excel
at producing accurate lip movements on a static image or videos of specific
people seen during the training phase. However, they fail to accurately morph
the lip mo... | 2020-08-23T11:01:25Z | 9 pages (including references), 3 figures, Accepted in ACM
Multimedia, 2020 | null | 10.1145/3394171.3413532 | null | null | null | null | null | null | null |
2,008.1057 | Example-Based Named Entity Recognition | ['Morteza Ziyadi', 'Yuting Sun', 'Abhishek Goswami', 'Jade Huang', 'Weizhu Chen'] | ['cs.CL', 'cs.IR'] | We present a novel approach to named entity recognition (NER) in the presence
of scarce data that we call example-based NER. Our train-free few-shot learning
approach takes inspiration from question-answering to identify entity spans in
a new and unseen domain. In comparison with the current state-of-the-art, the
propo... | 2020-08-24T17:18:24Z | 15 pages, 6 figures, 5 tables with appendix | null | null | null | null | null | null | null | null | null |
2,008.10831 | CDeC-Net: Composite Deformable Cascade Network for Table Detection in
Document Images | ['Madhav Agarwal', 'Ajoy Mondal', 'C. V. Jawahar'] | ['cs.CV'] | Localizing page elements/objects such as tables, figures, equations, etc. is
the primary step in extracting information from document images. We propose a
novel end-to-end trainable deep network, (CDeC-Net) for detecting tables
present in the documents. The proposed network consists of a multistage
extension of Mask R-... | 2020-08-25T05:53:59Z | 12 | null | null | CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images | ['Madhav Agarwal', 'Ajoy Mondal', 'C. V. Jawahar'] | 2,020 | International Conference on Pattern Recognition | 63 | 56 | ['Computer Science'] |
2,008.12014 | GREEK-BERT: The Greeks visiting Sesame Street | ['John Koutsikakis', 'Ilias Chalkidis', 'Prodromos Malakasiotis', 'Ion Androutsopoulos'] | ['cs.CL'] | Transformer-based language models, such as BERT and its variants, have
achieved state-of-the-art performance in several downstream natural language
processing (NLP) tasks on generic benchmark datasets (e.g., GLUE, SQUAD, RACE).
However, these models have mostly been applied to the resource-rich English
language. In thi... | 2020-08-27T09:36:14Z | 8 pages, 1 figure, 11th Hellenic Conference on Artificial
Intelligence (SETN 2020) | null | 10.1145/3411408.3411440 | null | null | null | null | null | null | null |
2,008.12272 | Monocular, One-stage, Regression of Multiple 3D People | ['Yu Sun', 'Qian Bao', 'Wu Liu', 'Yili Fu', 'Michael J. Black', 'Tao Mei'] | ['cs.CV'] | This paper focuses on the regression of multiple 3D people from a single RGB
image. Existing approaches predominantly follow a multi-stage pipeline that
first detects people in bounding boxes and then independently regresses their
3D body meshes. In contrast, we propose to Regress all meshes in a One-stage
fashion for ... | 2020-08-27T17:21:47Z | ICCV 2021, Code https://github.com/Arthur151/ROMP | null | null | Monocular, One-stage, Regression of Multiple 3D People | ['Yu Sun', 'Qian Bao', 'Wu Liu', 'Yili Fu', 'Michael J. Black', 'Tao Mei'] | 2,020 | IEEE International Conference on Computer Vision | 278 | 64 | ['Computer Science'] |
2,009.0059 | Summary-Source Proposition-level Alignment: Task, Datasets and
Supervised Baseline | ['Ori Ernst', 'Ori Shapira', 'Ramakanth Pasunuru', 'Michael Lepioshkin', 'Jacob Goldberger', 'Mohit Bansal', 'Ido Dagan'] | ['cs.CL'] | Aligning sentences in a reference summary with their counterparts in source
documents was shown as a useful auxiliary summarization task, notably for
generating training data for salience detection. Despite its assessed utility,
the alignment step was mostly approached with heuristic unsupervised methods,
typically ROU... | 2020-09-01T17:27:12Z | CoNLL 2021 | null | null | Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline | ['Ori Ernst', 'Ori Shapira', 'Ramakanth Pasunuru', 'Michael Lepioshkin', 'J. Goldberger', 'Mohit Bansal', 'Ido Dagan'] | 2,020 | Conference on Computational Natural Language Learning | 28 | 30 | ['Computer Science'] |
2,009.00713 | WaveGrad: Estimating Gradients for Waveform Generation | ['Nanxin Chen', 'Yu Zhang', 'Heiga Zen', 'Ron J. Weiss', 'Mohammad Norouzi', 'William Chan'] | ['eess.AS', 'cs.LG', 'cs.SD', 'stat.ML'] | This paper introduces WaveGrad, a conditional model for waveform generation
which estimates gradients of the data density. The model is built on prior work
on score matching and diffusion probabilistic models. It starts from a Gaussian
white noise signal and iteratively refines the signal via a gradient-based
sampler c... | 2020-09-02T17:44:10Z | null | null | null | WaveGrad: Estimating Gradients for Waveform Generation | ['Nanxin Chen', 'Yu Zhang', 'H. Zen', 'Ron J. Weiss', 'Mohammad Norouzi', 'William Chan'] | 2,020 | International Conference on Learning Representations | 795 | 66 | ['Computer Science', 'Engineering', 'Mathematics'] |
2,009.01325 | Learning to summarize from human feedback | ['Nisan Stiennon', 'Long Ouyang', 'Jeff Wu', 'Daniel M. Ziegler', 'Ryan Lowe', 'Chelsea Voss', 'Alec Radford', 'Dario Amodei', 'Paul Christiano'] | ['cs.CL', 'cs.AI', 'cs.LG'] | As language models become more powerful, training and evaluation are
increasingly bottlenecked by the data and metrics used for a particular task.
For example, summarization models are often trained to predict human reference
summaries and evaluated using ROUGE, but both of these metrics are rough
proxies for what we r... | 2020-09-02T19:54:41Z | NeurIPS 2020 | null | null | Learning to summarize from human feedback | ['Nisan Stiennon', 'Long Ouyang', 'Jeff Wu', 'Daniel M. Ziegler', 'Ryan J. Lowe', 'Chelsea Voss', 'Alec Radford', 'Dario Amodei', 'Paul Christiano'] | 2,020 | Neural Information Processing Systems | 2,195 | 84 | ['Computer Science'] |
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