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1,901.0286 | Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context | ['Zihang Dai', 'Zhilin Yang', 'Yiming Yang', 'Jaime Carbonell', 'Quoc V. Le', 'Ruslan Salakhutdinov'] | ['cs.LG', 'cs.CL', 'stat.ML'] | Transformers have a potential of learning longer-term dependency, but are
limited by a fixed-length context in the setting of language modeling. We
propose a novel neural architecture Transformer-XL that enables learning
dependency beyond a fixed length without disrupting temporal coherence. It
consists of a segment-le... | 2019-01-09T18:28:19Z | ACL 2019 long paper. Code and pretrained models are available at
https://github.com/kimiyoung/transformer-xl | null | null | Transformer-XL: Attentive Language Models beyond a Fixed-Length Context | ['Zihang Dai', 'Zhilin Yang', 'Yiming Yang', 'J. Carbonell', 'Quoc V. Le', 'R. Salakhutdinov'] | 2,019 | Annual Meeting of the Association for Computational Linguistics | 3,761 | 71 | ['Mathematics', 'Computer Science'] |
1,901.04085 | Passage Re-ranking with BERT | ['Rodrigo Nogueira', 'Kyunghyun Cho'] | ['cs.IR', 'cs.CL', 'cs.LG'] | Recently, neural models pretrained on a language modeling task, such as ELMo
(Peters et al., 2017), OpenAI GPT (Radford et al., 2018), and BERT (Devlin et
al., 2018), have achieved impressive results on various natural language
processing tasks such as question-answering and natural language inference. In
this paper, w... | 2019-01-13T23:27:58Z | null | null | null | Passage Re-ranking with BERT | ['Rodrigo Nogueira', 'Kyunghyun Cho'] | 2,019 | arXiv.org | 1,099 | 24 | ['Computer Science'] |
1,901.0478 | DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion | ['Chen Wang', 'Danfei Xu', 'Yuke Zhu', 'Roberto Martín-Martín', 'Cewu Lu', 'Li Fei-Fei', 'Silvio Savarese'] | ['cs.CV', 'cs.RO'] | A key technical challenge in performing 6D object pose estimation from RGB-D
image is to fully leverage the two complementary data sources. Prior works
either extract information from the RGB image and depth separately or use
costly post-processing steps, limiting their performances in highly cluttered
scenes and real-... | 2019-01-15T11:58:04Z | null | null | null | DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion | ['Chen Wang', 'Danfei Xu', 'Yuke Zhu', 'Roberto Martín-Martín', 'Cewu Lu', 'Li Fei-Fei', 'S. Savarese'] | 2,019 | Computer Vision and Pattern Recognition | 965 | 45 | ['Computer Science'] |
1,901.04856 | Sharing emotions at scale: The Vent dataset | ['Nikolaos Lykousas', 'Costantinos Patsakis', 'Andreas Kaltenbrunner', 'Vicenç Gómez'] | ['cs.SI', 'cs.HC'] | The continuous and increasing use of social media has enabled the expression
of human thoughts, opinions, and everyday actions publicly at an unprecedented
scale. We present the Vent dataset, the largest annotated dataset of text,
emotions, and social connections to date. It comprises more than 33 millions of
posts by ... | 2019-01-15T14:39:34Z | 9 pages, 12 figures, 2 tables. Accepted at the 13th International
AAAI Conference on Web and Social Media (ICWSM 2019) | null | null | null | null | null | null | null | null | null |
1,901.06081 | DeepOtsu: Document Enhancement and Binarization using Iterative Deep
Learning | ['Sheng He', 'Lambert Schomaker'] | ['cs.CV'] | This paper presents a novel iterative deep learning framework and apply it
for document enhancement and binarization. Unlike the traditional methods which
predict the binary label of each pixel on the input image, we train the neural
network to learn the degradations in document images and produce the uniform
images of... | 2019-01-18T04:23:51Z | Accepted by Pattern Recognition | null | 10.1016/j.patcog.2019.01.025 | null | null | null | null | null | null | null |
1,901.07042 | MIMIC-CXR-JPG, a large publicly available database of labeled chest
radiographs | ['Alistair E. W. Johnson', 'Tom J. Pollard', 'Nathaniel R. Greenbaum', 'Matthew P. Lungren', 'Chih-ying Deng', 'Yifan Peng', 'Zhiyong Lu', 'Roger G. Mark', 'Seth J. Berkowitz', 'Steven Horng'] | ['cs.CV', 'cs.LG', 'eess.IV'] | Chest radiography is an extremely powerful imaging modality, allowing for a
detailed inspection of a patient's thorax, but requiring specialized training
for proper interpretation. With the advent of high performance general purpose
computer vision algorithms, the accurate automated analysis of chest
radiographs is bec... | 2019-01-21T19:01:00Z | null | null | null | MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs | ['Alistair E. W. Johnson', 'T. Pollard', 'Nathaniel R. Greenbaum', 'M. Lungren', 'Chih-ying Deng', 'Yifan Peng', 'Zhiyong Lu', 'R. Mark', 'S. Berkowitz', 'S. Horng'] | 2,019 | null | 825 | 20 | ['Computer Science', 'Engineering'] |
1,901.07291 | Cross-lingual Language Model Pretraining | ['Guillaume Lample', 'Alexis Conneau'] | ['cs.CL'] | Recent studies have demonstrated the efficiency of generative pretraining for
English natural language understanding. In this work, we extend this approach
to multiple languages and show the effectiveness of cross-lingual pretraining.
We propose two methods to learn cross-lingual language models (XLMs): one
unsupervise... | 2019-01-22T13:22:34Z | null | null | null | Cross-lingual Language Model Pretraining | ['Guillaume Lample', 'Alexis Conneau'] | 2,019 | Neural Information Processing Systems | 2,753 | 52 | ['Computer Science'] |
1,901.07441 | PadChest: A large chest x-ray image dataset with multi-label annotated
reports | ['Aurelia Bustos', 'Antonio Pertusa', 'Jose-Maria Salinas', 'Maria de la Iglesia-Vayá'] | ['eess.IV', 'cs.CV', '92B20, 92C50, 68T50, 92B10'] | We present a labeled large-scale, high resolution chest x-ray dataset for the
automated exploration of medical images along with their associated reports.
This dataset includes more than 160,000 images obtained from 67,000 patients
that were interpreted and reported by radiologists at Hospital San Juan
Hospital (Spain)... | 2019-01-22T16:04:27Z | null | Med. Image Anal., 66 (2020), 101797 | 10.1016/j.media.2020.101797 | null | null | null | null | null | null | null |
1,901.08149 | TransferTransfo: A Transfer Learning Approach for Neural Network Based
Conversational Agents | ['Thomas Wolf', 'Victor Sanh', 'Julien Chaumond', 'Clement Delangue'] | ['cs.CL'] | We introduce a new approach to generative data-driven dialogue systems (e.g.
chatbots) called TransferTransfo which is a combination of a Transfer learning
based training scheme and a high-capacity Transformer model. Fine-tuning is
performed by using a multi-task objective which combines several unsupervised
prediction... | 2019-01-23T22:08:01Z | 6 pages, 2 figures, 2 tables, NeurIPS 2018 CAI Workshop | null | null | TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents | ['Thomas Wolf', 'Victor Sanh', 'Julien Chaumond', 'Clement Delangue'] | 2,019 | arXiv.org | 500 | 18 | ['Computer Science'] |
1,901.08746 | BioBERT: a pre-trained biomedical language representation model for
biomedical text mining | ['Jinhyuk Lee', 'Wonjin Yoon', 'Sungdong Kim', 'Donghyeon Kim', 'Sunkyu Kim', 'Chan Ho So', 'Jaewoo Kang'] | ['cs.CL'] | Biomedical text mining is becoming increasingly important as the number of
biomedical documents rapidly grows. With the progress in natural language
processing (NLP), extracting valuable information from biomedical literature
has gained popularity among researchers, and deep learning has boosted the
development of effe... | 2019-01-25T05:57:24Z | Bioinformatics | null | 10.1093/bioinformatics/btz682 | null | null | null | null | null | null | null |
1,901.10995 | Go-Explore: a New Approach for Hard-Exploration Problems | ['Adrien Ecoffet', 'Joost Huizinga', 'Joel Lehman', 'Kenneth O. Stanley', 'Jeff Clune'] | ['cs.LG', 'cs.AI', 'stat.ML'] | A grand challenge in reinforcement learning is intelligent exploration,
especially when rewards are sparse or deceptive. Two Atari games serve as
benchmarks for such hard-exploration domains: Montezuma's Revenge and Pitfall.
On both games, current RL algorithms perform poorly, even those with intrinsic
motivation, whic... | 2019-01-30T18:40:37Z | 37 pages, 14 figures; added references to Goyal et al. and Oh et al.,
updated reference to Colas et al; updated author emails; point readers to
updated paper | null | null | null | null | null | null | null | null | null |
1,902.00751 | Parameter-Efficient Transfer Learning for NLP | ['Neil Houlsby', 'Andrei Giurgiu', 'Stanislaw Jastrzebski', 'Bruna Morrone', 'Quentin de Laroussilhe', 'Andrea Gesmundo', 'Mona Attariyan', 'Sylvain Gelly'] | ['cs.LG', 'cs.CL', 'stat.ML'] | Fine-tuning large pre-trained models is an effective transfer mechanism in
NLP. However, in the presence of many downstream tasks, fine-tuning is
parameter inefficient: an entire new model is required for every task. As an
alternative, we propose transfer with adapter modules. Adapter modules yield a
compact and extens... | 2019-02-02T16:29:47Z | null | null | null | null | null | null | null | null | null | null |
1,902.06426 | 2017 Robotic Instrument Segmentation Challenge | ['Max Allan', 'Alex Shvets', 'Thomas Kurmann', 'Zichen Zhang', 'Rahul Duggal', 'Yun-Hsuan Su', 'Nicola Rieke', 'Iro Laina', 'Niveditha Kalavakonda', 'Sebastian Bodenstedt', 'Luis Herrera', 'Wenqi Li', 'Vladimir Iglovikov', 'Huoling Luo', 'Jian Yang', 'Danail Stoyanov', 'Lena Maier-Hein', 'Stefanie Speidel', 'Mahdi Aziz... | ['cs.CV'] | In mainstream computer vision and machine learning, public datasets such as
ImageNet, COCO and KITTI have helped drive enormous improvements by enabling
researchers to understand the strengths and limitations of different algorithms
via performance comparison. However, this type of approach has had limited
translation ... | 2019-02-18T07:08:36Z | null | null | null | null | null | null | null | null | null | null |
1,902.06634 | Contextual Encoder-Decoder Network for Visual Saliency Prediction | ['Alexander Kroner', 'Mario Senden', 'Kurt Driessens', 'Rainer Goebel'] | ['cs.CV'] | Predicting salient regions in natural images requires the detection of
objects that are present in a scene. To develop robust representations for this
challenging task, high-level visual features at multiple spatial scales must be
extracted and augmented with contextual information. However, existing models
aimed at ex... | 2019-02-18T16:15:25Z | Updated contact information | Neural Networks, 2020, Volume 129, Pages 261-270, ISSN 0893-6080 | 10.1016/j.neunet.2020.05.004 | null | null | null | null | null | null | null |
1,902.09212 | Deep High-Resolution Representation Learning for Human Pose Estimation | ['Ke Sun', 'Bin Xiao', 'Dong Liu', 'Jingdong Wang'] | ['cs.CV'] | This is an official pytorch implementation of Deep High-Resolution
Representation Learning for Human Pose Estimation. In this work, we are
interested in the human pose estimation problem with a focus on learning
reliable high-resolution representations. Most existing methods recover
high-resolution representations from... | 2019-02-25T11:55:28Z | accepted by CVPR2019 | null | null | null | null | null | null | null | null | null |
1,902.09476 | MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts | ['Sunil Mohan', 'Donghui Li'] | ['cs.CL', 'cs.LG'] | This paper presents the formal release of MedMentions, a new manually
annotated resource for the recognition of biomedical concepts. What
distinguishes MedMentions from other annotated biomedical corpora is its size
(over 4,000 abstracts and over 350,000 linked mentions), as well as the size of
the concept ontology (ov... | 2019-02-25T17:53:20Z | To appear in AKBC 2019 | null | null | null | null | null | null | null | null | null |
1,902.09811 | LaSO: Label-Set Operations networks for multi-label few-shot learning | ['Amit Alfassy', 'Leonid Karlinsky', 'Amit Aides', 'Joseph Shtok', 'Sivan Harary', 'Rogerio Feris', 'Raja Giryes', 'Alex M. Bronstein'] | ['cs.CV'] | Example synthesis is one of the leading methods to tackle the problem of
few-shot learning, where only a small number of samples per class are
available. However, current synthesis approaches only address the scenario of a
single category label per image. In this work, we propose a novel technique for
synthesizing samp... | 2019-02-26T09:12:09Z | null | null | null | null | null | null | null | null | null | null |
1,902.10191 | EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs | ['Aldo Pareja', 'Giacomo Domeniconi', 'Jie Chen', 'Tengfei Ma', 'Toyotaro Suzumura', 'Hiroki Kanezashi', 'Tim Kaler', 'Tao B. Schardl', 'Charles E. Leiserson'] | ['cs.LG', 'cs.SI', 'stat.ML'] | Graph representation learning resurges as a trending research subject owing
to the widespread use of deep learning for Euclidean data, which inspire
various creative designs of neural networks in the non-Euclidean domain,
particularly graphs. With the success of these graph neural networks (GNN) in
the static setting, ... | 2019-02-26T20:07:34Z | AAAI 2020. The code is available at https://github.com/IBM/EvolveGCN | null | null | null | null | null | null | null | null | null |
1,902.10909 | BERT for Joint Intent Classification and Slot Filling | ['Qian Chen', 'Zhu Zhuo', 'Wen Wang'] | ['cs.CL'] | Intent classification and slot filling are two essential tasks for natural
language understanding. They often suffer from small-scale human-labeled
training data, resulting in poor generalization capability, especially for rare
words. Recently a new language representation model, BERT (Bidirectional
Encoder Representat... | 2019-02-28T05:54:16Z | 4 pages, 1 figure | null | null | BERT for Joint Intent Classification and Slot Filling | ['Qian Chen', 'Zhu Zhuo', 'Wen Wang'] | 2,019 | arXiv.org | 558 | 26 | ['Computer Science'] |
1,903.00161 | DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning
Over Paragraphs | ['Dheeru Dua', 'Yizhong Wang', 'Pradeep Dasigi', 'Gabriel Stanovsky', 'Sameer Singh', 'Matt Gardner'] | ['cs.CL'] | Reading comprehension has recently seen rapid progress, with systems matching
humans on the most popular datasets for the task. However, a large body of work
has highlighted the brittleness of these systems, showing that there is much
work left to be done. We introduce a new English reading comprehension
benchmark, DRO... | 2019-03-01T05:32:01Z | null | null | null | null | null | null | null | null | null | null |
1,903.01435 | An Optimistic Acceleration of AMSGrad for Nonconvex Optimization | ['Jun-Kun Wang', 'Xiaoyun Li', 'Belhal Karimi', 'Ping Li'] | ['stat.ML', 'cs.LG'] | We propose a new variant of AMSGrad, a popular adaptive gradient based
optimization algorithm widely used for training deep neural networks. Our
algorithm adds prior knowledge about the sequence of consecutive mini-batch
gradients and leverages its underlying structure making the gradients
sequentially predictable. By ... | 2019-03-04T18:56:40Z | null | null | null | null | null | null | null | null | null | null |
1,903.02428 | Fast Graph Representation Learning with PyTorch Geometric | ['Matthias Fey', 'Jan Eric Lenssen'] | ['cs.LG', 'stat.ML'] | We introduce PyTorch Geometric, a library for deep learning on irregularly
structured input data such as graphs, point clouds and manifolds, built upon
PyTorch. In addition to general graph data structures and processing methods,
it contains a variety of recently published methods from the domains of
relational learnin... | 2019-03-06T14:50:02Z | ICLR 2019 (RLGM Workshop) | null | null | Fast Graph Representation Learning with PyTorch Geometric | ['Matthias Fey', 'J. E. Lenssen'] | 2,019 | arXiv.org | 4,381 | 51 | ['Computer Science', 'Mathematics'] |
1,903.05566 | Benchmarking Natural Language Understanding Services for building
Conversational Agents | ['Xingkun Liu', 'Arash Eshghi', 'Pawel Swietojanski', 'Verena Rieser'] | ['cs.CL', 'cs.LG'] | We have recently seen the emergence of several publicly available Natural
Language Understanding (NLU) toolkits, which map user utterances to structured,
but more abstract, Dialogue Act (DA) or Intent specifications, while making
this process accessible to the lay developer. In this paper, we present the
first wide cov... | 2019-03-13T16:08:46Z | Accepted by IWSDS2019 | null | null | null | null | null | null | null | null | null |
1,903.06586 | Selective Kernel Networks | ['Xiang Li', 'Wenhai Wang', 'Xiaolin Hu', 'Jian Yang'] | ['cs.CV'] | In standard Convolutional Neural Networks (CNNs), the receptive fields of
artificial neurons in each layer are designed to share the same size. It is
well-known in the neuroscience community that the receptive field size of
visual cortical neurons are modulated by the stimulus, which has been rarely
considered in const... | 2019-03-15T15:04:22Z | CVPR 2019 | null | null | Selective Kernel Networks | ['Xiang Li', 'Wenhai Wang', 'Xiaolin Hu', 'Jian Yang'] | 2,019 | Computer Vision and Pattern Recognition | 2,066 | 63 | ['Computer Science'] |
1,903.07291 | Semantic Image Synthesis with Spatially-Adaptive Normalization | ['Taesung Park', 'Ming-Yu Liu', 'Ting-Chun Wang', 'Jun-Yan Zhu'] | ['cs.CV', 'cs.AI', 'cs.GR', 'cs.LG', 'I.5; I.5.4; I.3.3'] | We propose spatially-adaptive normalization, a simple but effective layer for
synthesizing photorealistic images given an input semantic layout. Previous
methods directly feed the semantic layout as input to the deep network, which
is then processed through stacks of convolution, normalization, and
nonlinearity layers.... | 2019-03-18T08:12:23Z | Accepted as a CVPR 2019 oral paper | CVPR 2019 | null | null | null | null | null | null | null | null |
1,903.07785 | Cloze-driven Pretraining of Self-attention Networks | ['Alexei Baevski', 'Sergey Edunov', 'Yinhan Liu', 'Luke Zettlemoyer', 'Michael Auli'] | ['cs.CL'] | We present a new approach for pretraining a bi-directional transformer model
that provides significant performance gains across a variety of language
understanding problems. Our model solves a cloze-style word reconstruction
task, where each word is ablated and must be predicted given the rest of the
text. Experiments ... | 2019-03-19T01:19:06Z | null | null | null | Cloze-driven Pretraining of Self-attention Networks | ['Alexei Baevski', 'Sergey Edunov', 'Yinhan Liu', 'Luke Zettlemoyer', 'Michael Auli'] | 2,019 | Conference on Empirical Methods in Natural Language Processing | 198 | 41 | ['Computer Science'] |
1,903.08205 | Interactive segmentation of medical images through fully convolutional
neural networks | ['Tomas Sakinis', 'Fausto Milletari', 'Holger Roth', 'Panagiotis Korfiatis', 'Petro Kostandy', 'Kenneth Philbrick', 'Zeynettin Akkus', 'Ziyue Xu', 'Daguang Xu', 'Bradley J. Erickson'] | ['cs.CV'] | Image segmentation plays an essential role in medicine for both diagnostic
and interventional tasks. Segmentation approaches are either manual,
semi-automated or fully-automated. Manual segmentation offers full control over
the quality of the results, but is tedious, time consuming and prone to
operator bias. Fully aut... | 2019-03-19T18:28:49Z | null | null | null | null | null | null | null | null | null | null |
1,903.1052 | Micro-Batch Training with Batch-Channel Normalization and Weight
Standardization | ['Siyuan Qiao', 'Huiyu Wang', 'Chenxi Liu', 'Wei Shen', 'Alan Yuille'] | ['cs.CV', 'cs.LG'] | Batch Normalization (BN) has become an out-of-box technique to improve deep
network training. However, its effectiveness is limited for micro-batch
training, i.e., each GPU typically has only 1-2 images for training, which is
inevitable for many computer vision tasks, e.g., object detection and semantic
segmentation, c... | 2019-03-25T18:00:05Z | null | null | null | null | null | null | null | null | null | null |
1,903.10676 | SciBERT: A Pretrained Language Model for Scientific Text | ['Iz Beltagy', 'Kyle Lo', 'Arman Cohan'] | ['cs.CL'] | Obtaining large-scale annotated data for NLP tasks in the scientific domain
is challenging and expensive. We release SciBERT, a pretrained language model
based on BERT (Devlin et al., 2018) to address the lack of high-quality,
large-scale labeled scientific data. SciBERT leverages unsupervised pretraining
on a large mu... | 2019-03-26T05:11:46Z | https://github.com/allenai/scibert | EMNLP 2019 | null | null | null | null | null | null | null | null |
1,903.12261 | Benchmarking Neural Network Robustness to Common Corruptions and
Perturbations | ['Dan Hendrycks', 'Thomas Dietterich'] | ['cs.LG', 'cs.CV', 'stat.ML'] | In this paper we establish rigorous benchmarks for image classifier
robustness. Our first benchmark, ImageNet-C, standardizes and expands the
corruption robustness topic, while showing which classifiers are preferable in
safety-critical applications. Then we propose a new dataset called ImageNet-P
which enables researc... | 2019-03-28T20:56:37Z | ICLR 2019 camera-ready; datasets available at
https://github.com/hendrycks/robustness ; this article supersedes
arXiv:1807.01697 | null | null | null | null | null | null | null | null | null |
1,903.12519 | A Provable Defense for Deep Residual Networks | ['Matthew Mirman', 'Gagandeep Singh', 'Martin Vechev'] | ['cs.LG', 'cs.AI', 'cs.CR', 'cs.PL', 'stat.ML'] | We present a training system, which can provably defend significantly larger
neural networks than previously possible, including ResNet-34 and DenseNet-100.
Our approach is based on differentiable abstract interpretation and introduces
two novel concepts: (i) abstract layers for fine-tuning the precision and
scalabilit... | 2019-03-29T13:35:31Z | null | null | null | null | null | null | null | null | null | null |
1,904.00625 | Med3D: Transfer Learning for 3D Medical Image Analysis | ['Sihong Chen', 'Kai Ma', 'Yefeng Zheng'] | ['cs.CV'] | The performance on deep learning is significantly affected by volume of
training data. Models pre-trained from massive dataset such as ImageNet become
a powerful weapon for speeding up training convergence and improving accuracy.
Similarly, models based on large dataset are important for the development of
deep learnin... | 2019-04-01T08:14:29Z | null | null | null | null | null | null | null | null | null | null |
1,904.00962 | Large Batch Optimization for Deep Learning: Training BERT in 76 minutes | ['Yang You', 'Jing Li', 'Sashank Reddi', 'Jonathan Hseu', 'Sanjiv Kumar', 'Srinadh Bhojanapalli', 'Xiaodan Song', 'James Demmel', 'Kurt Keutzer', 'Cho-Jui Hsieh'] | ['cs.LG', 'cs.AI', 'cs.CL', 'stat.ML'] | Training large deep neural networks on massive datasets is computationally
very challenging. There has been recent surge in interest in using large batch
stochastic optimization methods to tackle this issue. The most prominent
algorithm in this line of research is LARS, which by employing layerwise
adaptive learning ra... | 2019-04-01T16:53:35Z | Published as a conference paper at ICLR 2020 | null | null | null | null | null | null | null | null | null |
1,904.0113 | PAWS: Paraphrase Adversaries from Word Scrambling | ['Yuan Zhang', 'Jason Baldridge', 'Luheng He'] | ['cs.CL'] | Existing paraphrase identification datasets lack sentence pairs that have
high lexical overlap without being paraphrases. Models trained on such data
fail to distinguish pairs like flights from New York to Florida and flights
from Florida to New York. This paper introduces PAWS (Paraphrase Adversaries
from Word Scrambl... | 2019-04-01T22:21:14Z | NAACL 2019 | null | null | PAWS: Paraphrase Adversaries from Word Scrambling | ['Yuan Zhang', 'Jason Baldridge', 'Luheng He'] | 2,019 | North American Chapter of the Association for Computational Linguistics | 545 | 36 | ['Computer Science'] |
1,904.01169 | Res2Net: A New Multi-scale Backbone Architecture | ['Shang-Hua Gao', 'Ming-Ming Cheng', 'Kai Zhao', 'Xin-Yu Zhang', 'Ming-Hsuan Yang', 'Philip Torr'] | ['cs.CV'] | Representing features at multiple scales is of great importance for numerous
vision tasks. Recent advances in backbone convolutional neural networks (CNNs)
continually demonstrate stronger multi-scale representation ability, leading to
consistent performance gains on a wide range of applications. However, most
existing... | 2019-04-02T01:56:34Z | 11 pages, 7 figures | IEEE TPAMI 2021 | 10.1109/TPAMI.2019.2938758 | Res2Net: A New Multi-Scale Backbone Architecture | ['Shanghua Gao', 'Ming-Ming Cheng', 'Kai Zhao', 'Xinyu Zhang', 'Ming-Hsuan Yang', 'Philip H. S. Torr'] | 2,019 | IEEE Transactions on Pattern Analysis and Machine Intelligence | 2,429 | 83 | ['Computer Science', 'Medicine'] |
1,904.01355 | FCOS: Fully Convolutional One-Stage Object Detection | ['Zhi Tian', 'Chunhua Shen', 'Hao Chen', 'Tong He'] | ['cs.CV'] | We propose a fully convolutional one-stage object detector (FCOS) to solve
object detection in a per-pixel prediction fashion, analogue to semantic
segmentation. Almost all state-of-the-art object detectors such as RetinaNet,
SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast,
our proposed dete... | 2019-04-02T11:56:36Z | Accepted to Proc. Int. Conf. Computer Vision 2019. 13 pages. Code is
available at: https://github.com/tianzhi0549/FCOS/ | null | null | FCOS: Fully Convolutional One-Stage Object Detection | ['Zhi Tian', 'Chunhua Shen', 'Hao Chen', 'Tong He'] | 2,019 | IEEE International Conference on Computer Vision | 5,038 | 37 | ['Computer Science'] |
1,904.01557 | Analysing Mathematical Reasoning Abilities of Neural Models | ['David Saxton', 'Edward Grefenstette', 'Felix Hill', 'Pushmeet Kohli'] | ['cs.LG', 'stat.ML'] | Mathematical reasoning---a core ability within human intelligence---presents
some unique challenges as a domain: we do not come to understand and solve
mathematical problems primarily on the back of experience and evidence, but on
the basis of inferring, learning, and exploiting laws, axioms, and symbol
manipulation ru... | 2019-04-02T17:26:41Z | null | null | null | null | null | null | null | null | null | null |
1,904.01941 | Character Region Awareness for Text Detection | ['Youngmin Baek', 'Bado Lee', 'Dongyoon Han', 'Sangdoo Yun', 'Hwalsuk Lee'] | ['cs.CV'] | Scene text detection methods based on neural networks have emerged recently
and have shown promising results. Previous methods trained with rigid
word-level bounding boxes exhibit limitations in representing the text region
in an arbitrary shape. In this paper, we propose a new scene text detection
method to effectivel... | 2019-04-03T12:00:33Z | 12 pages, 11 figures, Accepted by CVPR 2019 | null | null | null | null | null | null | null | null | null |
1,904.02099 | 75 Languages, 1 Model: Parsing Universal Dependencies Universally | ['Dan Kondratyuk', 'Milan Straka'] | ['cs.CL', 'cs.LG'] | We present UDify, a multilingual multi-task model capable of accurately
predicting universal part-of-speech, morphological features, lemmas, and
dependency trees simultaneously for all 124 Universal Dependencies treebanks
across 75 languages. By leveraging a multilingual BERT self-attention model
pretrained on 104 lang... | 2019-04-03T16:52:55Z | Accepted for publication at EMNLP 2019. 17 pages, 6 figures | null | null | 75 Languages, 1 Model: Parsing Universal Dependencies Universally | ['D. Kondratyuk'] | 2,019 | Conference on Empirical Methods in Natural Language Processing | 264 | 54 | ['Computer Science'] |
1,904.02285 | HoloDetect: Few-Shot Learning for Error Detection | ['Alireza Heidari', 'Joshua McGrath', 'Ihab F. Ilyas', 'Theodoros Rekatsinas'] | ['cs.DB'] | We introduce a few-shot learning framework for error detection. We show that
data augmentation (a form of weak supervision) is key to training high-quality,
ML-based error detection models that require minimal human involvement. Our
framework consists of two parts: (1) an expressive model to learn rich
representations ... | 2019-04-04T00:38:59Z | 18 pages, | ACM SIGMOD 2019 | 10.1145/3299869.3319888 | null | null | null | null | null | null | null |
1,904.02358 | Lightweight Image Super-Resolution with Adaptive Weighted Learning
Network | ['Chaofeng Wang', 'Zheng Li', 'Jun Shi'] | ['cs.CV', 'I.2.10; I.4'] | Deep learning has been successfully applied to the single-image
super-resolution (SISR) task with great performance in recent years. However,
most convolutional neural network based SR models require heavy computation,
which limit their real-world applications. In this work, a lightweight SR
network, named Adaptive Wei... | 2019-04-04T05:44:32Z | 9 pages, 6 figures | null | null | Lightweight Image Super-Resolution with Adaptive Weighted Learning Network | ['Chaofeng Wang', 'Zheng Li', 'Jun Shi'] | 2,019 | arXiv.org | 102 | 40 | ['Computer Science'] |
1,904.02701 | Libra R-CNN: Towards Balanced Learning for Object Detection | ['Jiangmiao Pang', 'Kai Chen', 'Jianping Shi', 'Huajun Feng', 'Wanli Ouyang', 'Dahua Lin'] | ['cs.CV'] | Compared with model architectures, the training process, which is also
crucial to the success of detectors, has received relatively less attention in
object detection. In this work, we carefully revisit the standard training
practice of detectors, and find that the detection performance is often limited
by the imbalanc... | 2019-04-04T17:58:22Z | To appear at CVPR 2019 | null | null | Libra R-CNN: Towards Balanced Learning for Object Detection | ['Jiangmiao Pang', 'Kai Chen', 'Jianping Shi', 'H. Feng', 'Wanli Ouyang', 'Dahua Lin'] | 2,019 | Computer Vision and Pattern Recognition | 1,297 | 38 | ['Computer Science'] |
1,904.02877 | Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4
Hours | ['Dimitrios Stamoulis', 'Ruizhou Ding', 'Di Wang', 'Dimitrios Lymberopoulos', 'Bodhi Priyantha', 'Jie Liu', 'Diana Marculescu'] | ['cs.LG', 'cs.CV', 'stat.ML'] | Can we automatically design a Convolutional Network (ConvNet) with the
highest image classification accuracy under the runtime constraint of a mobile
device? Neural architecture search (NAS) has revolutionized the design of
hardware-efficient ConvNets by automating this process. However, the NAS
problem remains challen... | 2019-04-05T05:49:41Z | null | null | null | null | null | null | null | null | null | null |
1,904.02882 | LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech | ['Heiga Zen', 'Viet Dang', 'Rob Clark', 'Yu Zhang', 'Ron J. Weiss', 'Ye Jia', 'Zhifeng Chen', 'Yonghui Wu'] | ['cs.SD', 'eess.AS'] | This paper introduces a new speech corpus called "LibriTTS" designed for
text-to-speech use. It is derived from the original audio and text materials of
the LibriSpeech corpus, which has been used for training and evaluating
automatic speech recognition systems. The new corpus inherits desired
properties of the LibriSp... | 2019-04-05T06:05:00Z | Submitted for Interspeech 2019, 7 pages | null | null | null | null | null | null | null | null | null |
1,904.03323 | Publicly Available Clinical BERT Embeddings | ['Emily Alsentzer', 'John R. Murphy', 'Willie Boag', 'Wei-Hung Weng', 'Di Jin', 'Tristan Naumann', 'Matthew B. A. McDermott'] | ['cs.CL'] | Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT
(Devlin et al., 2018) have dramatically improved performance for many natural
language processing (NLP) tasks in recent months. However, these models have
been minimally explored on specialty corpora, such as clinical text; moreover,
in the cl... | 2019-04-06T00:34:39Z | Clinical Natural Language Processing (ClinicalNLP) Workshop at NAACL
2019 | null | null | null | null | null | null | null | null | null |
1,904.03493 | VATEX: A Large-Scale, High-Quality Multilingual Dataset for
Video-and-Language Research | ['Xin Wang', 'Jiawei Wu', 'Junkun Chen', 'Lei Li', 'Yuan-Fang Wang', 'William Yang Wang'] | ['cs.CV', 'cs.CL', 'cs.LG'] | We present a new large-scale multilingual video description dataset, VATEX,
which contains over 41,250 videos and 825,000 captions in both English and
Chinese. Among the captions, there are over 206,000 English-Chinese parallel
translation pairs. Compared to the widely-used MSR-VTT dataset, VATEX is
multilingual, large... | 2019-04-06T16:50:31Z | ICCV 2019 Oral. 17 pages, 14 figures, 6 tables (updated the VATEX
website link: vatex-challenge.org) | null | null | null | null | null | null | null | null | null |
1,904.0367 | Speech Model Pre-training for End-to-End Spoken Language Understanding | ['Loren Lugosch', 'Mirco Ravanelli', 'Patrick Ignoto', 'Vikrant Singh Tomar', 'Yoshua Bengio'] | ['eess.AS', 'cs.CL', 'cs.LG', 'cs.SD'] | Whereas conventional spoken language understanding (SLU) systems map speech
to text, and then text to intent, end-to-end SLU systems map speech directly to
intent through a single trainable model. Achieving high accuracy with these
end-to-end models without a large amount of training data is difficult. We
propose a met... | 2019-04-07T15:24:32Z | Accepted to Interspeech 2019 | null | null | Speech Model Pre-training for End-to-End Spoken Language Understanding | ['Loren Lugosch', 'M. Ravanelli', 'Patrick Ignoto', 'Vikrant Singh Tomar', 'Yoshua Bengio'] | 2,019 | Interspeech | 356 | 43 | ['Computer Science', 'Engineering'] |
1,904.03969 | Issue Framing in Online Discussion Fora | ['Mareike Hartmann', 'Tallulah Jansen', 'Isabelle Augenstein', 'Anders Søgaard'] | ['cs.CL', 'cs.LG'] | In online discussion fora, speakers often make arguments for or against
something, say birth control, by highlighting certain aspects of the topic. In
social science, this is referred to as issue framing. In this paper, we
introduce a new issue frame annotated corpus of online discussions. We explore
to what extent mod... | 2019-04-08T11:36:53Z | To appear in NAACL-HLT 2019 | null | null | null | null | null | null | null | null | null |
1,904.04971 | CondConv: Conditionally Parameterized Convolutions for Efficient
Inference | ['Brandon Yang', 'Gabriel Bender', 'Quoc V. Le', 'Jiquan Ngiam'] | ['cs.CV', 'cs.AI', 'cs.LG'] | Convolutional layers are one of the basic building blocks of modern deep
neural networks. One fundamental assumption is that convolutional kernels
should be shared for all examples in a dataset. We propose conditionally
parameterized convolutions (CondConv), which learn specialized convolutional
kernels for each exampl... | 2019-04-10T01:46:48Z | null | NeurIPS 2019 | null | null | null | null | null | null | null | null |
1,904.06472 | A Repository of Conversational Datasets | ['Matthew Henderson', 'Paweł Budzianowski', 'Iñigo Casanueva', 'Sam Coope', 'Daniela Gerz', 'Girish Kumar', 'Nikola Mrkšić', 'Georgios Spithourakis', 'Pei-Hao Su', 'Ivan Vulić', 'Tsung-Hsien Wen'] | ['cs.CL'] | Progress in Machine Learning is often driven by the availability of large
datasets, and consistent evaluation metrics for comparing modeling approaches.
To this end, we present a repository of conversational datasets consisting of
hundreds of millions of examples, and a standardised evaluation procedure for
conversatio... | 2019-04-13T02:59:48Z | null | Proceedings of the Workshop on NLP for Conversational AI (2019) | null | null | null | null | null | null | null | null |
1,904.07396 | Real Image Denoising with Feature Attention | ['Saeed Anwar', 'Nick Barnes'] | ['cs.CV', 'cs.LG'] | Deep convolutional neural networks perform better on images containing
spatially invariant noise (synthetic noise); however, their performance is
limited on real-noisy photographs and requires multiple stage network modeling.
To advance the practicability of denoising algorithms, this paper proposes a
novel single-stag... | 2019-04-16T01:55:08Z | Accepted in ICCV (Oral), 2019 | null | null | null | null | null | null | null | null | null |
1,904.07733 | Subjective Assessment of Text Complexity: A Dataset for German Language | ['Babak Naderi', 'Salar Mohtaj', 'Kaspar Ensikat', 'Sebastian Möller'] | ['cs.CL'] | This paper presents TextComplexityDE, a dataset consisting of 1000 sentences
in German language taken from 23 Wikipedia articles in 3 different
article-genres to be used for developing text-complexity predictor models and
automatic text simplification in German language. The dataset includes
subjective assessment of di... | 2019-04-16T14:39:21Z | null | null | null | null | null | null | null | null | null | null |
1,904.0785 | Objects as Points | ['Xingyi Zhou', 'Dequan Wang', 'Philipp Krähenbühl'] | ['cs.CV'] | Detection identifies objects as axis-aligned boxes in an image. Most
successful object detectors enumerate a nearly exhaustive list of potential
object locations and classify each. This is wasteful, inefficient, and requires
additional post-processing. In this paper, we take a different approach. We
model an object as ... | 2019-04-16T17:54:26Z | 12 pages, 5 figures | null | null | Objects as Points | ['Xingyi Zhou', 'Dequan Wang', 'Philipp Krähenbühl'] | 2,019 | arXiv.org | 3,266 | 64 | ['Computer Science'] |
1,904.08375 | Document Expansion by Query Prediction | ['Rodrigo Nogueira', 'Wei Yang', 'Jimmy Lin', 'Kyunghyun Cho'] | ['cs.IR', 'cs.LG'] | One technique to improve the retrieval effectiveness of a search engine is to
expand documents with terms that are related or representative of the
documents' content.From the perspective of a question answering system, this
might comprise questions the document can potentially answer. Following this
observation, we pr... | 2019-04-17T17:20:14Z | null | null | null | null | null | null | null | null | null | null |
1,904.08779 | SpecAugment: A Simple Data Augmentation Method for Automatic Speech
Recognition | ['Daniel S. Park', 'William Chan', 'Yu Zhang', 'Chung-Cheng Chiu', 'Barret Zoph', 'Ekin D. Cubuk', 'Quoc V. Le'] | ['eess.AS', 'cs.CL', 'cs.LG', 'cs.SD', 'stat.ML'] | We present SpecAugment, a simple data augmentation method for speech
recognition. SpecAugment is applied directly to the feature inputs of a neural
network (i.e., filter bank coefficients). The augmentation policy consists of
warping the features, masking blocks of frequency channels, and masking blocks
of time steps. ... | 2019-04-18T17:53:38Z | 5 pages, 3 figures, 6 tables; v3: references added | Proc. Interspeech 2019, 2613-2617 | 10.21437/Interspeech.2019-2680 | null | null | null | null | null | null | null |
1,904.09077 | Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT | ['Shijie Wu', 'Mark Dredze'] | ['cs.CL'] | Pretrained contextual representation models (Peters et al., 2018; Devlin et
al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new
release of BERT (Devlin, 2018) includes a model simultaneously pretrained on
104 languages with impressive performance for zero-shot cross-lingual transfer
on a natura... | 2019-04-19T04:45:44Z | EMNLP 2019 Camera Ready | null | null | Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT | ['Shijie Wu', 'Mark Dredze'] | 2,019 | Conference on Empirical Methods in Natural Language Processing | 681 | 46 | ['Computer Science'] |
1,904.09223 | ERNIE: Enhanced Representation through Knowledge Integration | ['Yu Sun', 'Shuohuan Wang', 'Yukun Li', 'Shikun Feng', 'Xuyi Chen', 'Han Zhang', 'Xin Tian', 'Danxiang Zhu', 'Hao Tian', 'Hua Wu'] | ['cs.CL'] | We present a novel language representation model enhanced by knowledge called
ERNIE (Enhanced Representation through kNowledge IntEgration). Inspired by the
masking strategy of BERT, ERNIE is designed to learn language representation
enhanced by knowledge masking strategies, which includes entity-level masking
and phra... | 2019-04-19T15:10:56Z | 8 pages | null | null | ERNIE: Enhanced Representation through Knowledge Integration | ['Yu Sun', 'Shuohuan Wang', 'Yukun Li', 'Shikun Feng', 'Xuyi Chen', 'Han Zhang', 'Xin Tian', 'Danxiang Zhu', 'Hao Tian', 'Hua Wu'] | 2,019 | arXiv.org | 907 | 23 | ['Computer Science'] |
1,904.09675 | BERTScore: Evaluating Text Generation with BERT | ['Tianyi Zhang', 'Varsha Kishore', 'Felix Wu', 'Kilian Q. Weinberger', 'Yoav Artzi'] | ['cs.CL'] | We propose BERTScore, an automatic evaluation metric for text generation.
Analogously to common metrics, BERTScore computes a similarity score for each
token in the candidate sentence with each token in the reference sentence.
However, instead of exact matches, we compute token similarity using contextual
embeddings. W... | 2019-04-21T23:08:53Z | Code available at https://github.com/Tiiiger/bert_score; To appear in
ICLR2020 | null | null | null | null | null | null | null | null | null |
1,904.09728 | SocialIQA: Commonsense Reasoning about Social Interactions | ['Maarten Sap', 'Hannah Rashkin', 'Derek Chen', 'Ronan LeBras', 'Yejin Choi'] | ['cs.CL'] | We introduce Social IQa, the first largescale benchmark for commonsense
reasoning about social situations. Social IQa contains 38,000 multiple choice
questions for probing emotional and social intelligence in a variety of
everyday situations (e.g., Q: "Jordan wanted to tell Tracy a secret, so Jordan
leaned towards Trac... | 2019-04-22T05:36:37Z | the first two authors contributed equally; accepted to EMNLP 2019;
camera ready version | null | null | null | null | null | null | null | null | null |
1,904.0973 | An Energy and GPU-Computation Efficient Backbone Network for Real-Time
Object Detection | ['Youngwan Lee', 'Joong-won Hwang', 'Sangrok Lee', 'Yuseok Bae', 'Jongyoul Park'] | ['cs.CV'] | As DenseNet conserves intermediate features with diverse receptive fields by
aggregating them with dense connection, it shows good performance on the object
detection task. Although feature reuse enables DenseNet to produce strong
features with a small number of model parameters and FLOPs, the detector with
DenseNet ba... | 2019-04-22T05:45:57Z | CVPR2019 CEFRL Workshop | null | null | An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection | ['Youngwan Lee', 'Joong-won Hwang', 'Sangrok Lee', 'Yuseok Bae', 'Jongyoul Park'] | 2,019 | 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | 374 | 33 | ['Computer Science'] |
1,904.09751 | The Curious Case of Neural Text Degeneration | ['Ari Holtzman', 'Jan Buys', 'Li Du', 'Maxwell Forbes', 'Yejin Choi'] | ['cs.CL'] | Despite considerable advancements with deep neural language models, the
enigma of neural text degeneration persists when these models are tested as
text generators. The counter-intuitive empirical observation is that even
though the use of likelihood as training objective leads to high quality models
for a broad range ... | 2019-04-22T07:17:18Z | Published in ICLR 2020 | null | null | null | null | null | null | null | null | null |
1,904.10635 | Better Automatic Evaluation of Open-Domain Dialogue Systems with
Contextualized Embeddings | ['Sarik Ghazarian', 'Johnny Tian-Zheng Wei', 'Aram Galstyan', 'Nanyun Peng'] | ['cs.CL'] | Despite advances in open-domain dialogue systems, automatic evaluation of
such systems is still a challenging problem. Traditional reference-based
metrics such as BLEU are ineffective because there could be many valid
responses for a given context that share no common words with reference
responses. A recent work propo... | 2019-04-24T04:16:44Z | 8 pages, 2 figures, NAACL 2019 Methods for Optimizing and Evaluating
Neural Language Generation (NeuralGen workshop) | null | null | null | null | null | null | null | null | null |
1,904.11486 | Making Convolutional Networks Shift-Invariant Again | ['Richard Zhang'] | ['cs.CV', 'cs.LG'] | Modern convolutional networks are not shift-invariant, as small input shifts
or translations can cause drastic changes in the output. Commonly used
downsampling methods, such as max-pooling, strided-convolution, and
average-pooling, ignore the sampling theorem. The well-known signal processing
fix is anti-aliasing by l... | 2019-04-25T17:56:21Z | Accepted to ICML 2019 | null | null | Making Convolutional Networks Shift-Invariant Again | ['Richard Zhang'] | 2,019 | International Conference on Machine Learning | 801 | 80 | ['Computer Science'] |
1,904.11491 | Local Relation Networks for Image Recognition | ['Han Hu', 'Zheng Zhang', 'Zhenda Xie', 'Stephen Lin'] | ['cs.CV', 'cs.AI', 'cs.LG'] | The convolution layer has been the dominant feature extractor in computer
vision for years. However, the spatial aggregation in convolution is basically
a pattern matching process that applies fixed filters which are inefficient at
modeling visual elements with varying spatial distributions. This paper
presents a new i... | 2019-04-25T17:59:35Z | null | null | null | Local Relation Networks for Image Recognition | ['Han Hu', 'Zheng Zhang', 'Zhenda Xie', 'Stephen Lin'] | 2,019 | IEEE International Conference on Computer Vision | 503 | 39 | ['Computer Science'] |
1,904.11492 | GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond | ['Yue Cao', 'Jiarui Xu', 'Stephen Lin', 'Fangyun Wei', 'Han Hu'] | ['cs.CV', 'cs.AI', 'cs.LG'] | The Non-Local Network (NLNet) presents a pioneering approach for capturing
long-range dependencies, via aggregating query-specific global context to each
query position. However, through a rigorous empirical analysis, we have found
that the global contexts modeled by non-local network are almost the same for
different ... | 2019-04-25T17:59:42Z | null | null | null | null | null | null | null | null | null | null |
1,904.12848 | Unsupervised Data Augmentation for Consistency Training | ['Qizhe Xie', 'Zihang Dai', 'Eduard Hovy', 'Minh-Thang Luong', 'Quoc V. Le'] | ['cs.LG', 'cs.AI', 'cs.CL', 'cs.CV', 'stat.ML'] | Semi-supervised learning lately has shown much promise in improving deep
learning models when labeled data is scarce. Common among recent approaches is
the use of consistency training on a large amount of unlabeled data to
constrain model predictions to be invariant to input noise. In this work, we
present a new perspe... | 2019-04-29T17:56:59Z | NeurIPS 2020 | null | null | null | null | null | null | null | null | null |
1,905.00537 | SuperGLUE: A Stickier Benchmark for General-Purpose Language
Understanding Systems | ['Alex Wang', 'Yada Pruksachatkun', 'Nikita Nangia', 'Amanpreet Singh', 'Julian Michael', 'Felix Hill', 'Omer Levy', 'Samuel R. Bowman'] | ['cs.CL', 'cs.AI'] | In the last year, new models and methods for pretraining and transfer
learning have driven striking performance improvements across a range of
language understanding tasks. The GLUE benchmark, introduced a little over one
year ago, offers a single-number metric that summarizes progress on a diverse
set of such tasks, b... | 2019-05-02T00:41:50Z | NeurIPS 2019, super.gluebenchmark.com updating acknowledegments | null | null | SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems | ['Alex Wang', 'Yada Pruksachatkun', 'Nikita Nangia', 'Amanpreet Singh', 'Julian Michael', 'Felix Hill', 'Omer Levy', 'Samuel R. Bowman'] | 2,019 | Neural Information Processing Systems | 2,331 | 86 | ['Computer Science'] |
1,905.00546 | Billion-scale semi-supervised learning for image classification | ['I. Zeki Yalniz', 'Hervé Jégou', 'Kan Chen', 'Manohar Paluri', 'Dhruv Mahajan'] | ['cs.CV'] | This paper presents a study of semi-supervised learning with large
convolutional networks. We propose a pipeline, based on a teacher/student
paradigm, that leverages a large collection of unlabelled images (up to 1
billion). Our main goal is to improve the performance for a given target
architecture, like ResNet-50 or ... | 2019-05-02T02:08:18Z | null | null | null | null | null | null | null | null | null | null |
1,905.00641 | RetinaFace: Single-stage Dense Face Localisation in the Wild | ['Jiankang Deng', 'Jia Guo', 'Yuxiang Zhou', 'Jinke Yu', 'Irene Kotsia', 'Stefanos Zafeiriou'] | ['cs.CV'] | Though tremendous strides have been made in uncontrolled face detection,
accurate and efficient face localisation in the wild remains an open challenge.
This paper presents a robust single-stage face detector, named RetinaFace,
which performs pixel-wise face localisation on various scales of faces by
taking advantages ... | 2019-05-02T09:45:23Z | null | null | null | null | null | null | null | null | null | null |
1,905.00953 | Omni-Scale Feature Learning for Person Re-Identification | ['Kaiyang Zhou', 'Yongxin Yang', 'Andrea Cavallaro', 'Tao Xiang'] | ['cs.CV'] | As an instance-level recognition problem, person re-identification (ReID)
relies on discriminative features, which not only capture different spatial
scales but also encapsulate an arbitrary combination of multiple scales. We
call features of both homogeneous and heterogeneous scales omni-scale features.
In this paper,... | 2019-05-02T20:42:26Z | ICCV 2019; This version adds additional training recipes for
practitioners | null | null | Omni-Scale Feature Learning for Person Re-Identification | ['Kaiyang Zhou', 'Yongxin Yang', 'A. Cavallaro', 'T. Xiang'] | 2,019 | IEEE International Conference on Computer Vision | 839 | 93 | ['Computer Science'] |
1,905.01969 | Poly-encoders: Transformer Architectures and Pre-training Strategies for
Fast and Accurate Multi-sentence Scoring | ['Samuel Humeau', 'Kurt Shuster', 'Marie-Anne Lachaux', 'Jason Weston'] | ['cs.CL', 'cs.AI'] | The use of deep pre-trained bidirectional transformers has led to remarkable
progress in a number of applications (Devlin et al., 2018). For tasks that make
pairwise comparisons between sequences, matching a given input with a
corresponding label, two approaches are common: Cross-encoders performing full
self-attention... | 2019-04-22T02:18:00Z | ICLR 2020 | null | null | Poly-encoders: Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring | ['Samuel Humeau', 'Kurt Shuster', 'M. Lachaux', 'J. Weston'] | 2,019 | International Conference on Learning Representations | 289 | 34 | ['Computer Science'] |
1,905.02244 | Searching for MobileNetV3 | ['Andrew Howard', 'Mark Sandler', 'Grace Chu', 'Liang-Chieh Chen', 'Bo Chen', 'Mingxing Tan', 'Weijun Wang', 'Yukun Zhu', 'Ruoming Pang', 'Vijay Vasudevan', 'Quoc V. Le', 'Hartwig Adam'] | ['cs.CV'] | We present the next generation of MobileNets based on a combination of
complementary search techniques as well as a novel architecture design.
MobileNetV3 is tuned to mobile phone CPUs through a combination of
hardware-aware network architecture search (NAS) complemented by the NetAdapt
algorithm and then subsequently ... | 2019-05-06T19:38:31Z | ICCV 2019 | null | null | null | null | null | null | null | null | null |
1,905.0245 | MASS: Masked Sequence to Sequence Pre-training for Language Generation | ['Kaitao Song', 'Xu Tan', 'Tao Qin', 'Jianfeng Lu', 'Tie-Yan Liu'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Pre-training and fine-tuning, e.g., BERT, have achieved great success in
language understanding by transferring knowledge from rich-resource
pre-training task to the low/zero-resource downstream tasks. Inspired by the
success of BERT, we propose MAsked Sequence to Sequence pre-training (MASS) for
the encoder-decoder ba... | 2019-05-07T10:13:04Z | Accepted by ICML 2019 | null | null | MASS: Masked Sequence to Sequence Pre-training for Language Generation | ['Kaitao Song', 'Xu Tan', 'Tao Qin', 'Jianfeng Lu', 'Tie-Yan Liu'] | 2,019 | International Conference on Machine Learning | 967 | 60 | ['Computer Science'] |
1,905.04899 | CutMix: Regularization Strategy to Train Strong Classifiers with
Localizable Features | ['Sangdoo Yun', 'Dongyoon Han', 'Seong Joon Oh', 'Sanghyuk Chun', 'Junsuk Choe', 'Youngjoon Yoo'] | ['cs.CV', 'cs.LG'] | Regional dropout strategies have been proposed to enhance the performance of
convolutional neural network classifiers. They have proved to be effective for
guiding the model to attend on less discriminative parts of objects (e.g. leg
as opposed to head of a person), thereby letting the network generalize better
and hav... | 2019-05-13T08:10:22Z | Accepted at ICCV 2019 (oral talk). 14 pages, 5 figures | null | null | null | null | null | null | null | null | null |
1,905.05583 | How to Fine-Tune BERT for Text Classification? | ['Chi Sun', 'Xipeng Qiu', 'Yige Xu', 'Xuanjing Huang'] | ['cs.CL'] | Language model pre-training has proven to be useful in learning universal
language representations. As a state-of-the-art language model pre-training
model, BERT (Bidirectional Encoder Representations from Transformers) has
achieved amazing results in many language understanding tasks. In this paper,
we conduct exhaust... | 2019-05-14T13:17:26Z | null | null | null | null | null | null | null | null | null | null |
1,905.057 | Learning meters of Arabic and English poems with Recurrent Neural
Networks: a step forward for language understanding and synthesis | ['Waleed A. Yousef', 'Omar M. Ibrahime', 'Taha M. Madbouly', 'Moustafa A. Mahmoud'] | ['cs.CL', 'cs.AI', 'cs.LG', 'stat.ML'] | Recognizing a piece of writing as a poem or prose is usually easy for the
majority of people; however, only specialists can determine which meter a poem
belongs to. In this paper, we build Recurrent Neural Network (RNN) models that
can classify poems according to their meters from plain text. The input text is
encoded ... | 2019-05-07T21:14:03Z | null | null | null | null | null | null | null | null | null | null |
1,905.05879 | AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss | ['Kaizhi Qian', 'Yang Zhang', 'Shiyu Chang', 'Xuesong Yang', 'Mark Hasegawa-Johnson'] | ['eess.AS', 'cs.AI', 'cs.LG', 'cs.SD', 'stat.ML'] | Non-parallel many-to-many voice conversion, as well as zero-shot voice
conversion, remain under-explored areas. Deep style transfer algorithms, such
as generative adversarial networks (GAN) and conditional variational
autoencoder (CVAE), are being applied as new solutions in this field. However,
GAN training is sophist... | 2019-05-14T23:19:04Z | To Appear in Thirty-sixth International Conference on Machine
Learning (ICML 2019) | null | null | null | null | null | null | null | null | null |
1,905.0629 | A Surprisingly Robust Trick for Winograd Schema Challenge | ['Vid Kocijan', 'Ana-Maria Cretu', 'Oana-Maria Camburu', 'Yordan Yordanov', 'Thomas Lukasiewicz'] | ['cs.CL'] | The Winograd Schema Challenge (WSC) dataset WSC273 and its inference
counterpart WNLI are popular benchmarks for natural language understanding and
commonsense reasoning. In this paper, we show that the performance of three
language models on WSC273 strongly improves when fine-tuned on a similar
pronoun disambiguation ... | 2019-05-15T16:47:11Z | Appeared as part of the ACL 2019 conference | null | 10.18653/v1/P19-1478 | A Surprisingly Robust Trick for the Winograd Schema Challenge | ['Vid Kocijan', 'Ana-Maria Cretu', 'Oana-Maria Camburu', 'Yordan Yordanov', 'Thomas Lukasiewicz'] | 2,019 | Annual Meeting of the Association for Computational Linguistics | 101 | 22 | ['Computer Science'] |
1,905.07213 | Adaptation of Deep Bidirectional Multilingual Transformers for Russian
Language | ['Yuri Kuratov', 'Mikhail Arkhipov'] | ['cs.CL'] | The paper introduces methods of adaptation of multilingual masked language
models for a specific language. Pre-trained bidirectional language models show
state-of-the-art performance on a wide range of tasks including reading
comprehension, natural language inference, and sentiment analysis. At the
moment there are two... | 2019-05-17T11:39:21Z | null | null | null | Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language | ['Yuri Kuratov', 'M. Arkhipov'] | 2,019 | arXiv.org | 275 | 18 | ['Computer Science'] |
1,905.0783 | HellaSwag: Can a Machine Really Finish Your Sentence? | ['Rowan Zellers', 'Ari Holtzman', 'Yonatan Bisk', 'Ali Farhadi', 'Yejin Choi'] | ['cs.CL'] | Recent work by Zellers et al. (2018) introduced a new task of commonsense
natural language inference: given an event description such as "A woman sits at
a piano," a machine must select the most likely followup: "She sets her fingers
on the keys." With the introduction of BERT, near human-level performance was
reached.... | 2019-05-19T23:57:23Z | ACL 2019. Project page at https://rowanzellers.com/hellaswag | null | null | HellaSwag: Can a Machine Really Finish Your Sentence? | ['Rowan Zellers', 'Ari Holtzman', 'Yonatan Bisk', 'Ali Farhadi', 'Yejin Choi'] | 2,019 | Annual Meeting of the Association for Computational Linguistics | 2,538 | 22 | ['Computer Science'] |
1,905.09263 | FastSpeech: Fast, Robust and Controllable Text to Speech | ['Yi Ren', 'Yangjun Ruan', 'Xu Tan', 'Tao Qin', 'Sheng Zhao', 'Zhou Zhao', 'Tie-Yan Liu'] | ['cs.CL', 'cs.LG', 'cs.SD', 'eess.AS'] | Neural network based end-to-end text to speech (TTS) has significantly
improved the quality of synthesized speech. Prominent methods (e.g., Tacotron
2) usually first generate mel-spectrogram from text, and then synthesize speech
from the mel-spectrogram using vocoder such as WaveNet. Compared with
traditional concatena... | 2019-05-22T17:50:21Z | Accepted by NeurIPS2019 | null | null | null | null | null | null | null | null | null |
1,905.09381 | Learning to Prove Theorems via Interacting with Proof Assistants | ['Kaiyu Yang', 'Jia Deng'] | ['cs.LO', 'cs.AI', 'cs.LG', 'stat.ML'] | Humans prove theorems by relying on substantial high-level reasoning and
problem-specific insights. Proof assistants offer a formalism that resembles
human mathematical reasoning, representing theorems in higher-order logic and
proofs as high-level tactics. However, human experts have to construct proofs
manually by en... | 2019-05-21T17:56:02Z | Accepted to ICML 2019 | null | null | null | null | null | null | null | null | null |
1,905.10044 | BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions | ['Christopher Clark', 'Kenton Lee', 'Ming-Wei Chang', 'Tom Kwiatkowski', 'Michael Collins', 'Kristina Toutanova'] | ['cs.CL'] | In this paper we study yes/no questions that are naturally occurring ---
meaning that they are generated in unprompted and unconstrained settings. We
build a reading comprehension dataset, BoolQ, of such questions, and show that
they are unexpectedly challenging. They often query for complex, non-factoid
information, a... | 2019-05-24T05:48:49Z | In NAACL 2019 | null | null | BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions | ['Christopher Clark', 'Kenton Lee', 'Ming-Wei Chang', 'T. Kwiatkowski', 'Michael Collins', 'Kristina Toutanova'] | 2,019 | North American Chapter of the Association for Computational Linguistics | 1,565 | 50 | ['Computer Science'] |
1,905.10892 | Extreme Multi-Label Legal Text Classification: A case study in EU
Legislation | ['Ilias Chalkidis', 'Manos Fergadiotis', 'Prodromos Malakasiotis', 'Nikolaos Aletras', 'Ion Androutsopoulos'] | ['cs.CL'] | We consider the task of Extreme Multi-Label Text Classification (XMTC) in the
legal domain. We release a new dataset of 57k legislative documents from
EURLEX, the European Union's public document database, annotated with concepts
from EUROVOC, a multidisciplinary thesaurus. The dataset is substantially
larger than prev... | 2019-05-26T21:50:15Z | 10 pages, long paper at NLLP Workshop of NAACL-HLT 2019 | null | null | null | null | null | null | null | null | null |
1,905.11901 | Revisiting Low-Resource Neural Machine Translation: A Case Study | ['Rico Sennrich', 'Biao Zhang'] | ['cs.CL'] | It has been shown that the performance of neural machine translation (NMT)
drops starkly in low-resource conditions, underperforming phrase-based
statistical machine translation (PBSMT) and requiring large amounts of
auxiliary data to achieve competitive results. In this paper, we re-assess the
validity of these result... | 2019-05-28T15:59:21Z | to appear at ACL 2019 | null | null | Revisiting Low-Resource Neural Machine Translation: A Case Study | ['Rico Sennrich', 'Biao Zhang'] | 2,019 | Annual Meeting of the Association for Computational Linguistics | 223 | 56 | ['Computer Science'] |
1,905.11946 | EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks | ['Mingxing Tan', 'Quoc V. Le'] | ['cs.LG', 'cs.CV', 'stat.ML'] | Convolutional Neural Networks (ConvNets) are commonly developed at a fixed
resource budget, and then scaled up for better accuracy if more resources are
available. In this paper, we systematically study model scaling and identify
that carefully balancing network depth, width, and resolution can lead to
better performan... | 2019-05-28T17:05:32Z | ICML 2019 | International Conference on Machine Learning, 2019 | null | null | null | null | null | null | null | null |
1,905.12516 | Racial Bias in Hate Speech and Abusive Language Detection Datasets | ['Thomas Davidson', 'Debasmita Bhattacharya', 'Ingmar Weber'] | ['cs.CL', 'cs.LG'] | Technologies for abusive language detection are being developed and applied
with little consideration of their potential biases. We examine racial bias in
five different sets of Twitter data annotated for hate speech and abusive
language. We train classifiers on these datasets and compare the predictions of
these class... | 2019-05-29T15:12:58Z | To appear in the proceedings of the Third Abusive Language Workshop
(https://sites.google.com/view/alw3/) at the Annual Meeting for the
Association for Computational Linguistics 2019. Please cite the published
version | null | null | null | null | null | null | null | null | null |
1,905.13648 | Scene Text Visual Question Answering | ['Ali Furkan Biten', 'Ruben Tito', 'Andres Mafla', 'Lluis Gomez', 'Marçal Rusiñol', 'Ernest Valveny', 'C. V. Jawahar', 'Dimosthenis Karatzas'] | ['cs.CV'] | Current visual question answering datasets do not consider the rich semantic
information conveyed by text within an image. In this work, we present a new
dataset, ST-VQA, that aims to highlight the importance of exploiting high-level
semantic information present in images as textual cues in the VQA process. We
use this... | 2019-05-31T14:47:55Z | International Conference on Computer Vision (ICCV 2019) | null | null | Scene Text Visual Question Answering | ['Ali Furkan Biten', 'Rubèn Pérez Tito', 'Andrés Mafla', 'Lluís Gómez', 'Marçal Rusiñol', 'Ernest Valveny', 'C. V. Jawahar', 'Dimosthenis Karatzas'] | 2,019 | IEEE International Conference on Computer Vision | 361 | 68 | ['Computer Science'] |
1,906.01502 | How multilingual is Multilingual BERT? | ['Telmo Pires', 'Eva Schlinger', 'Dan Garrette'] | ['cs.CL', 'cs.AI', 'cs.LG'] | In this paper, we show that Multilingual BERT (M-BERT), released by Devlin et
al. (2018) as a single language model pre-trained from monolingual corpora in
104 languages, is surprisingly good at zero-shot cross-lingual model transfer,
in which task-specific annotations in one language are used to fine-tune the
model fo... | 2019-06-04T15:12:47Z | null | null | null | How Multilingual is Multilingual BERT? | ['Telmo Pires', 'Eva Schlinger', 'Dan Garrette'] | 2,019 | Annual Meeting of the Association for Computational Linguistics | 1,418 | 19 | ['Computer Science'] |
1,906.01569 | Sequence Tagging with Contextual and Non-Contextual Subword
Representations: A Multilingual Evaluation | ['Benjamin Heinzerling', 'Michael Strube'] | ['cs.CL'] | Pretrained contextual and non-contextual subword embeddings have become
available in over 250 languages, allowing massively multilingual NLP. However,
while there is no dearth of pretrained embeddings, the distinct lack of
systematic evaluations makes it difficult for practitioners to choose between
them. In this work,... | 2019-06-04T16:36:53Z | ACL 2019 | null | null | Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation | ['Benjamin Heinzerling', 'M. Strube'] | 2,019 | Annual Meeting of the Association for Computational Linguistics | 36 | 45 | ['Computer Science'] |
1,906.01591 | Pair State Transfer | ['Qiuting Chen', 'Chris Godsil'] | ['math.CO', 'math-ph', 'math.MP', 'quant-ph'] | Let $L$ denote the Laplacian matrix of a graph $G$. We study continuous
quantum walks on $G$ defined by the transition matrix
$U(t)=\exp\left(itL\right)$. The initial state is of the pair state form,
$e_a-e_b$ with $a,b$ being any two vertices of $G$. We provide two ways to
construct infinite families of graphs that ha... | 2019-06-04T17:09:10Z | null | null | null | null | null | null | null | null | null | null |
1,906.01749 | Multi-News: a Large-Scale Multi-Document Summarization Dataset and
Abstractive Hierarchical Model | ['Alexander R. Fabbri', 'Irene Li', 'Tianwei She', 'Suyi Li', 'Dragomir R. Radev'] | ['cs.CL'] | Automatic generation of summaries from multiple news articles is a valuable
tool as the number of online publications grows rapidly. Single document
summarization (SDS) systems have benefited from advances in neural
encoder-decoder model thanks to the availability of large datasets. However,
multi-document summarizatio... | 2019-06-04T23:00:43Z | ACL 2019, 57th Annual Meeting of the Association for Computational
Linguistics, Florence, Italy, 2019 | null | null | Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model | ['Alexander R. Fabbri', 'Irene Li', 'Tianwei She', 'Suyi Li', 'Dragomir R. Radev'] | 2,019 | Annual Meeting of the Association for Computational Linguistics | 590 | 46 | ['Computer Science'] |
1,906.02045 | The FRENK Datasets of Socially Unacceptable Discourse in Slovene and
English | ['Nikola Ljubešić', 'Darja Fišer', 'Tomaž Erjavec'] | ['cs.CL'] | In this paper we present datasets of Facebook comment threads to mainstream
media posts in Slovene and English developed inside the Slovene national
project FRENK which cover two topics, migrants and LGBT, and are manually
annotated for different types of socially unacceptable discourse (SUD). The
main advantages of th... | 2019-06-05T14:23:01Z | null | null | null | null | null | null | null | null | null | null |
1,906.02192 | Large-Scale Multi-Label Text Classification on EU Legislation | ['Ilias Chalkidis', 'Manos Fergadiotis', 'Prodromos Malakasiotis', 'Ion Androutsopoulos'] | ['cs.CL'] | We consider Large-Scale Multi-Label Text Classification (LMTC) in the legal
domain. We release a new dataset of 57k legislative documents from EURLEX,
annotated with ~4.3k EUROVOC labels, which is suitable for LMTC, few- and
zero-shot learning. Experimenting with several neural classifiers, we show that
BIGRUs with lab... | 2019-06-05T14:41:01Z | 9 pages, short paper at ACL 2019. arXiv admin note: text overlap with
arXiv:1905.10892 | null | null | null | null | null | null | null | null | null |
1,906.02243 | Energy and Policy Considerations for Deep Learning in NLP | ['Emma Strubell', 'Ananya Ganesh', 'Andrew McCallum'] | ['cs.CL'] | Recent progress in hardware and methodology for training neural networks has
ushered in a new generation of large networks trained on abundant data. These
models have obtained notable gains in accuracy across many NLP tasks. However,
these accuracy improvements depend on the availability of exceptionally large
computat... | 2019-06-05T18:40:53Z | In the 57th Annual Meeting of the Association for Computational
Linguistics (ACL). Florence, Italy. July 2019 | null | null | null | null | null | null | null | null | null |
1,906.02467 | ActivityNet-QA: A Dataset for Understanding Complex Web Videos via
Question Answering | ['Zhou Yu', 'Dejing Xu', 'Jun Yu', 'Ting Yu', 'Zhou Zhao', 'Yueting Zhuang', 'Dacheng Tao'] | ['cs.CV'] | Recent developments in modeling language and vision have been successfully
applied to image question answering. It is both crucial and natural to extend
this research direction to the video domain for video question answering
(VideoQA). Compared to the image domain where large scale and fully annotated
benchmark datase... | 2019-06-06T08:08:14Z | Accepted at AAAI 2019 | null | null | ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering | ['Zhou Yu', 'D. Xu', 'Jun Yu', 'Ting Yu', 'Zhou Zhao', 'Yueting Zhuang', 'D. Tao'] | 2,019 | AAAI Conference on Artificial Intelligence | 478 | 43 | ['Computer Science'] |
1,906.02569 | Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild | ['Abubakar Abid', 'Ali Abdalla', 'Ali Abid', 'Dawood Khan', 'Abdulrahman Alfozan', 'James Zou'] | ['cs.LG', 'cs.HC', 'stat.ML'] | Accessibility is a major challenge of machine learning (ML). Typical ML
models are built by specialists and require specialized hardware/software as
well as ML experience to validate. This makes it challenging for non-technical
collaborators and endpoint users (e.g. physicians) to easily provide feedback
on model devel... | 2019-06-06T13:18:47Z | Presented at 2019 ICML Workshop on Human in the Loop Learning (HILL
2019), Long Beach, USA | null | null | Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild | ['Abubakar Abid', 'Ali Abdalla', 'Ali Abid', 'Dawood Khan', 'Abdulrahman Alfozan', 'James Y. Zou'] | 2,019 | arXiv.org | 213 | 10 | ['Computer Science', 'Mathematics'] |
1,906.02659 | Does Object Recognition Work for Everyone? | ['Terrance DeVries', 'Ishan Misra', 'Changhan Wang', 'Laurens van der Maaten'] | ['cs.CV', 'cs.LG'] | The paper analyzes the accuracy of publicly available object-recognition
systems on a geographically diverse dataset. This dataset contains household
items and was designed to have a more representative geographical coverage than
commonly used image datasets in object recognition. We find that the systems
perform relat... | 2019-06-06T16:00:18Z | null | null | null | Does Object Recognition Work for Everyone? | ['Terrance Devries', 'Ishan Misra', 'Changhan Wang', 'L. Maaten'] | 2,019 | CVPR Workshops | 265 | 43 | ['Computer Science'] |
1,906.02762 | Understanding and Improving Transformer From a Multi-Particle Dynamic
System Point of View | ['Yiping Lu', 'Zhuohan Li', 'Di He', 'Zhiqing Sun', 'Bin Dong', 'Tao Qin', 'Liwei Wang', 'Tie-Yan Liu'] | ['cs.LG', 'cs.CL', 'stat.ML'] | The Transformer architecture is widely used in natural language processing.
Despite its success, the design principle of the Transformer remains elusive.
In this paper, we provide a novel perspective towards understanding the
architecture: we show that the Transformer can be mathematically interpreted as
a numerical Or... | 2019-06-06T18:10:08Z | null | null | null | null | null | null | null | null | null | null |
1,906.03402 | Effective Use of Variational Embedding Capacity in Expressive End-to-End
Speech Synthesis | ['Eric Battenberg', 'Soroosh Mariooryad', 'Daisy Stanton', 'RJ Skerry-Ryan', 'Matt Shannon', 'David Kao', 'Tom Bagby'] | ['cs.CL', 'cs.LG', 'cs.SD', 'eess.AS'] | Recent work has explored sequence-to-sequence latent variable models for
expressive speech synthesis (supporting control and transfer of prosody and
style), but has not presented a coherent framework for understanding the
trade-offs between the competing methods. In this paper, we propose embedding
capacity (the amount... | 2019-06-08T06:59:56Z | Submitted to ICLR 2020 | null | null | null | null | null | null | null | null | null |
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