2020.04.29.txt

==========New Papers==========
1, TITLE:       Extending Multilingual BERT to Low-Resource Languages
http://arxiv.org/abs/2004.13640
AUTHORS:        Zihan Wang ; Karthikeyan K ; Stephen Mayhew ; Dan Roth
HIGHLIGHT:      In this paper, we propose a simple but effective approach to extend M-BERT (E-BERT) so that it can benefit any new language, and show that our approach benefits languages that are already in M-BERT as well.

2, TITLE:       Joint Keyphrase Chunking and Salience Ranking with BERT
http://arxiv.org/abs/2004.13639
AUTHORS:        Si Sun ; Chenyan Xiong ; Zhenghao Liu ; Zhiyuan Liu ; Jie Bao
COMMENTS:       6 pages, 3 figures, 2 tables
HIGHLIGHT:      This paper presents BERT-JointKPE, a multi-task BERT-based model for keyphrase extraction.

3, TITLE:       Recipes for building an open-domain chatbot
http://arxiv.org/abs/2004.13637
AUTHORS:        Stephen Roller ; Emily Dinan ; Naman Goyal ; Da Ju ; Mary Williamson ; Yinhan Liu ; Jing Xu ; Myle Ott ; Kurt Shuster ; Eric M. Smith ; Y-Lan Boureau ; Jason Weston
HIGHLIGHT:      We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models and code publicly available under the collective name Blender.

4, TITLE:       KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion
http://arxiv.org/abs/2004.13631
AUTHORS:        Jie Zhou ; Xin Lv ; Cheng Yang ; Zhiyuan Liu ; Juanzi Li ; Maosong Sun
HIGHLIGHT:      In this paper, we propose large-scale datasets extracted from Wikidata, which provide more size-balanced concept graphs and abundant cross-view links. Based on the datasets, we further propose a benchmark to test the ability of existing models on knowledge abstraction, concretization and completion (KACC).

5, TITLE:       Event Extraction by Answering (Almost) Natural Questions
http://arxiv.org/abs/2004.13625
AUTHORS:        Xinya Du ; Claire Cardie
HIGHLIGHT:      To avoid this issue, we introduce a new paradigm for event extraction by formulating it as a question answering (QA) task, which extracts the event arguments in an end-to-end manner.

6, TITLE:       Exploring Self-attention for Image Recognition
http://arxiv.org/abs/2004.13621
AUTHORS:        Hengshuang Zhao ; Jiaya Jia ; Vladlen Koltun
COMMENTS:       CVPR 2020, Code available at https://github.com/hszhao/SAN
HIGHLIGHT:      Recent work has shown that self-attention can serve as a basic building block for image recognition models.

7, TITLE:       Visual Grounding of Learned Physical Models
http://arxiv.org/abs/2004.13664
AUTHORS:        Yunzhu Li ; Toru Lin ; Kexin Yi ; Daniel Bear ; Daniel L. K. Yamins ; Jiajun Wu ; Joshua B. Tenenbaum ; Antonio Torralba
COMMENTS:       The second and the third authors contributed equally to this paper, and are listed in alphabetical order. Project Page: http://visual-physics-grounding.csail.mit.edu/
HIGHLIGHT:      In this work, we present a neural model that simultaneously reasons about physics and make future predictions based on visual and dynamics priors.

8, TITLE:       LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network
http://arxiv.org/abs/2004.13659
AUTHORS:        Wanjun Zhong ; Duyu Tang ; Zhangyin Feng ; Nan Duan ; Ming Zhou ; Ming Gong ; Linjun Shou ; Daxin Jiang ; Jiahai Wang ; Jian Yin
COMMENTS:       13 pages; 7 figures; Accepted by ACL2020 as a long paper
HIGHLIGHT:      In this work, we propose LogicalFactChecker, a neural network approach capable of leveraging logical operations for fact checking.

9, TITLE:       Sample-Efficient Model-based Actor-Critic for an Interactive Dialogue Task
http://arxiv.org/abs/2004.13657
AUTHORS:        Katya Kudashkina ; Valliappa Chockalingam ; Graham W. Taylor ; Michael Bowling
HIGHLIGHT:      As a solution path, we present a model-based reinforcement learning algorithm for an interactive dialogue task.

10, TITLE:      Pitfalls of learning a reward function online
http://arxiv.org/abs/2004.13654
AUTHORS:        Stuart Armstrong ; Jan Leike ; Laurent Orseau ; Shane Legg
HIGHLIGHT:      We formally introduce two desirable properties: the first is `unriggability', which prevents the agent from steering the learning process in the direction of a reward function that is easier to optimise.

11, TITLE:      Event-based Robotic Grasping Detection with Neuromorphic Vision Sensor and Event-Stream Dataset
http://arxiv.org/abs/2004.13652
AUTHORS:        Bin Li ; Hu Cao ; Zhongnan Qu ; Yingbai Hu ; Zhenke Wang ; Zichen Liang
COMMENTS:       14 pages, 16 figures, 26 references
HIGHLIGHT:      In this work, we consider the problem of detecting robotic grasps in a moving camera view of a scene containing objects. We construct a robotic grasping dataset named \emph{Event-Stream Dataset} with 91 objects. This work provides a large-scale and well-annotated dataset, and promotes the neuromorphic vision applications in agile robot.

12, TITLE:      Real-Time Apple Detection System Using Embedded Systems With Hardware Accelerators: An Edge AI Application
http://arxiv.org/abs/2004.13410
AUTHORS:        Vittorio Mazzia ; Francesco Salvetti ; Aleem Khaliq ; Marcello Chiaberge
HIGHLIGHT:      In this study, a real-time embedded solution inspired from "Edge AI" is proposed for apple detection with the implementation of YOLOv3-tiny algorithm on various embedded platforms such as Raspberry Pi 3 B+ in combination with Intel Movidius Neural Computing Stick (NCS), Nvidia's Jetson Nano and Jetson AGX Xavier.

13, TITLE:      Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
http://arxiv.org/abs/2004.13649
AUTHORS:        Ilya Kostrikov ; Denis Yarats ; Rob Fergus
HIGHLIGHT:      We propose a simple data augmentation technique that can be applied to standard model-free reinforcement learning algorithms, enabling robust learning directly from pixels without the need for auxiliary losses or pre-training.

14, TITLE:      Identification of Cervical Pathology using Adversarial Neural Networks
http://arxiv.org/abs/2004.13406
AUTHORS:        Abhilash Nandy ; Rachana Sathish ; Debdoot Sheet
COMMENTS:       9 pages, 10 images, 5th MedImage Workshop of 11th Indian Conference on Computer Vision, Graphics and Image Processing, Hyderabad, India, 2018
HIGHLIGHT:      In this paper, we propose a convolutional autoencoder based framework, having an architecture similar to SegNet which is trained in an adversarial fashion for classifying images of the cervix acquired using a colposcope.

15, TITLE:      Unnatural Language Processing: Bridging the Gap Between Synthetic and Natural Language Data
http://arxiv.org/abs/2004.13645
AUTHORS:        Alana Marzoev ; Samuel Madden ; M. Frans Kaashoek ; Michael Cafarella ; Jacob Andreas
HIGHLIGHT:      We address this problem by introducing a general purpose technique for ``simulation-to-real'' transfer in language understanding problems with a delimited set of target behaviors, making it possible to develop models that can interpret natural utterances without natural training data.

16, TITLE:      Style-transfer GANs for bridging the domain gap in synthetic pose estimator training
http://arxiv.org/abs/2004.13681
AUTHORS:        Pavel Rojtberg ; Thomas Pöllabauer ; Arjan Kuijper
HIGHLIGHT:      We propose to adopt general-purpose GAN models for pixel-level image translation, allowing to formulate the domain gap itself as a learning problem.

17, TITLE:      Improving Sample Efficiency and Multi-Agent Communication in RL-based Train Rescheduling
http://arxiv.org/abs/2004.13439
AUTHORS:        Dano Roost ; Ralph Meier ; Stephan Huschauer ; Erik Nygren ; Adrian Egli ; Andreas Weiler ; Thilo Stadelmann
COMMENTS:       Accepted for publication at the 7th Swiss Conference on Data Science (SDS 2020)
HIGHLIGHT:      We present preliminary results from our sixth placed entry to the Flatland international competition for train rescheduling, including two improvements for optimized reinforcement learning (RL) training efficiency, and two hypotheses with respect to the prospect of deep RL for complex real-world control tasks: first, that current state of the art policy gradient methods seem inappropriate in the domain of high-consequence environments; second, that learning explicit communication actions (an emerging machine-to-machine language, so to speak) might offer a remedy.

18, TITLE:      Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for Offensive Language Detection
http://arxiv.org/abs/2004.13432
AUTHORS:        Wenliang Dai ; Tiezheng Yu ; Zihan Liu ; Pascale Fung
COMMENTS:       Submitted to SemEval-2020 Workshop
HIGHLIGHT:      In this paper, we build an offensive language detection system, which combines multi-task learning with BERT-based models.

19, TITLE:      Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction
http://arxiv.org/abs/2004.13674
AUTHORS:        Jie Cai ; Zibo Meng ; Chiu Man Ho
HIGHLIGHT:      In this paper, we propose a Residual Channel Attention-Generative Adversarial Network(RCA-GAN) to solve these problems.

20, TITLE:      Angle-based Search Space Shrinking for Neural Architecture Search
http://arxiv.org/abs/2004.13431
AUTHORS:        Yiming Hu ; Yuding Liang ; Zichao Guo ; Ruosi Wan ; Xiangyu Zhang ; Yichen Wei ; Qingyi Gu ; Jian Sun
COMMENTS:       14 pages
HIGHLIGHT:      In this work, we present a simple and general search space shrinking method, called Angle-Based search space Shrinking (ABS), for Neural Architecture Search (NAS).

21, TITLE:      Active Learning for Coreference Resolution using Discrete Annotation
http://arxiv.org/abs/2004.13671
AUTHORS:        Belinda Li ; Gabriel Stanovsky ; Luke Zettlemoyer
COMMENTS:       12 pages, 7 figures, ACL 2020
HIGHLIGHT:      We improve upon pairwise annotation for active learning in coreference resolution, by asking annotators to identify mention antecedents if a presented mention pair is deemed not coreferent.

22, TITLE:      A novel Region of Interest Extraction Layer for Instance Segmentation
http://arxiv.org/abs/2004.13665
AUTHORS:        Leonardo Rossi ; Akbar Karimi ; Andrea Prati
HIGHLIGHT:      A comprehensive ablation study at component level is conducted to find the best set of algorithms and parameters for the GRoIE layer.

23, TITLE:      KoParadigm: A Korean Conjugation Paradigm Generator
http://arxiv.org/abs/2004.13221
AUTHORS:        Kyubyong Park
HIGHLIGHT:      In this paper we introduce a Korean (verb) conjugation paradigm generator, dubbed KoParadigm.

24, TITLE:      DiVA: Diverse Visual Feature Aggregation forDeep Metric Learning
http://arxiv.org/abs/2004.13458
AUTHORS:        Timo Milbich ; Karsten Roth ; Homanga Bharadhwaj ; Samarth Sinha ; Yoshua Bengio ; Björn Ommer ; Joseph Paul Cohen
COMMENTS:       18 pages
HIGHLIGHT:      To this end, we propose and study multiple complementary learning tasks, targeting conceptually different data relationships by only resorting to the available training samples and labels of a standard DML setting.

25, TITLE:      Inferring Temporal Compositions of Actions Using Probabilistic Automata
http://arxiv.org/abs/2004.13217
AUTHORS:        Rodrigo Santa Cruz ; Anoop Cherian ; Basura Fernando ; Dylan Campbell ; Stephen Gould
COMMENTS:       Accepted in Workshop on Compositionality in Computer Vision at CVPR, 2020
HIGHLIGHT:      This paper presents a framework to recognize temporal compositions of atomic actions in videos.

26, TITLE:      An Effective Transition-based Model for Discontinuous NER
http://arxiv.org/abs/2004.13454
AUTHORS:        Xiang Dai ; Sarvnaz Karimi ; Ben Hachey ; Cecile Paris
COMMENTS:       ACL 2020
HIGHLIGHT:      We propose a simple, effective transition-based model with generic neural encoding for discontinuous NER.

27, TITLE:      DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification
http://arxiv.org/abs/2004.13455
AUTHORS:        Lianwei Wu ; Yuan Rao ; Yongqiang Zhao ; Hao Liang ; Ambreen Nazir
COMMENTS:       ACL 2020
HIGHLIGHT:      In this paper, we propose a Decision Tree-based Co-Attention model (DTCA) to discover evidence for explainable claim verification.

28, TITLE:      DRU-net: An Efficient Deep Convolutional Neural Network for Medical Image Segmentation
http://arxiv.org/abs/2004.13453
AUTHORS:        Mina Jafari ; Dorothee Auer ; Susan Francis ; Jonathan Garibaldi ; Xin Chen
COMMENTS:       Accepted for publication at IEEE International Symposium on Biomedical Imaging (ISBI) 2020, 5 pages, 3 figures
HIGHLIGHT:      In this paper, we propose an efficient network architecture by considering advantages of both networks.

29, TITLE:      Leveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstruction
http://arxiv.org/abs/2004.13449
AUTHORS:        Yana Hasson ; Bugra Tekin ; Federica Bogo ; Ivan Laptev ; Marc Pollefeys ; Cordelia Schmid
COMMENTS:       CVPR 2020. See the project webpage at https://hassony2.github.io/handobjectconsist.html
HIGHLIGHT:      To overcome this challenge we present a method to leverage photometric consistency across time when annotations are only available for a sparse subset of frames in a video.

30, TITLE:      A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization
http://arxiv.org/abs/2004.13203
AUTHORS:        Graham Neubig ; Shruti Rijhwani ; Alexis Palmer ; Jordan MacKenzie ; Hilaria Cruz ; Xinjian Li ; Matthew Lee ; Aditi Chaudhary ; Luke Gessler ; Steven Abney ; Shirley Anugrah Hayati ; Antonios Anastasopoulos ; Olga Zamaraeva ; Emily Prud'hommeaux ; Jennette Child ; Sara Child ; Rebecca Knowles ; Sarah Moeller ; Jeffrey Micher ; Yiyuan Li ; Sydney Zink ; Mengzhou Xia ; Roshan S Sharma ; Patrick Littell
COMMENTS:       Accepted at SLTU-CCURL 2020
HIGHLIGHT:      This paper reports the results of this workshop, including issues discussed, and various conceived and implemented technologies for nine languages: Arapaho, Cayuga, Inuktitut, Irish Gaelic, Kidaw'ida, Kwak'wala, Ojibwe, San Juan Quiahije Chatino, and Seneca.

31, TITLE:      Graph2Plan: Learning Floorplan Generation from Layout Graphs
http://arxiv.org/abs/2004.13204
AUTHORS:        Ruizhen Hu ; Zeyu Huang ; Yuhan Tang ; Oliver van Kaick ; Hao Zhang ; Hui Huang
HIGHLIGHT:      We introduce a learning framework for automated floorplan generation which combines generative modeling using deep neural networks and user-in-the-loop designs to enable human users to provide sparse design constraints.

32, TITLE:      The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions
http://arxiv.org/abs/2004.13606
AUTHORS:        Xiang Zhou ; Yixin Nie ; Hao Tan ; Mohit Bansal
COMMENTS:       13 pages
HIGHLIGHT:      We find that the performance of state-of-the-art models on Natural Language Inference (NLI) and Reading Comprehension (RC) analysis/stress sets can be highly unstable.

33, TITLE:      Don't Let Me Be Misunderstood: Comparing Intentions and Perceptions in Online Discussions
http://arxiv.org/abs/2004.13609
AUTHORS:        Jonathan P. Chang ; Justin Cheng ; Cristian Danescu-Niculescu-Mizil
COMMENTS:       Proceedings of The Web Conference (WWW) 2020
HIGHLIGHT:      In this work, we present a computational framework for exploring and comparing both perspectives in online public discussions.

34, TITLE:      A Novel Attention-based Aggregation Function to Combine Vision and Language
http://arxiv.org/abs/2004.13073
AUTHORS:        Matteo Stefanini ; Marcella Cornia ; Lorenzo Baraldi ; Rita Cucchiara
HIGHLIGHT:      In this paper, we propose a novel fully-attentive reduction method for vision and language.

35, TITLE:      Context-aware Helpfulness Prediction for Online Product Reviews
http://arxiv.org/abs/2004.13078
AUTHORS:        Iyiola E. Olatunji ; Xin Li ; Wai Lam
COMMENTS:       Published as a proceeding paper in AIRS 2019
HIGHLIGHT:      In this paper, we propose a neural deep learning model that predicts the helpfulness score of a review.

36, TITLE:      The Problem of Fragmented Occlusion in Object Detection
http://arxiv.org/abs/2004.13076
AUTHORS:        Julian Pegoraro ; Roman Pflugfelder
COMMENTS:       accepted by the Austrian Joint Computer Vision and Robotics Workshop 2020 (https://acvrw20.ist.tugraz.at)
HIGHLIGHT:      This paper presents an analysis of state-of-the-art detectors with imagery of green borders and proposes to train Mask R-CNN on new training data which captures explicitly the problem of fragmented occlusion.

37, TITLE:      Self-Supervised Attention Learning for Depth and Ego-motion Estimation
http://arxiv.org/abs/2004.13077
AUTHORS:        Assem Sadek ; Boris Chidlovskii
HIGHLIGHT:      We address the problem of depth and ego-motion estimation from image sequences.

38, TITLE:      A generic and efficient convolutional neural network accelerator using HLS for a system on chip design
http://arxiv.org/abs/2004.13075
AUTHORS:        Kim Bjerge ; Jonathan Schougaard ; Daniel Ejnar Larsen
COMMENTS:       18 pages, 15 figures
HIGHLIGHT:      This paper presents a generic convolutional neural network accelerator (CNNA) for a system on chip design (SoC).

39, TITLE:      Compact retail shelf segmentation for mobile deployment
http://arxiv.org/abs/2004.13094
AUTHORS:        Pratyush Kumar ; Muktabh Mayank Srivastava
COMMENTS:       10 pages
HIGHLIGHT:      In this paper, we work on one such common problem in the retail industries - Shelf segmentation.

40, TITLE:      Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems
http://arxiv.org/abs/2004.13245
AUTHORS:        Dai Hoang Tran ; Quan Z. Sheng ; Wei Emma Zhang ; Salma Abdalla Hamad ; Munazza Zaib ; Nguyen H. Tran ; Lina Yao ; Nguyen Lu Dang Khoa
COMMENTS:       7 pages, 3 figures, 1 table
HIGHLIGHT:      In this work, we provide a summarization of the recent evolution of CRS, where deep learning approaches are applied to CRS and have produced fruitful results.

41, TITLE:      On the Reliability of Test Collections for Evaluating Systems of Different Types
http://arxiv.org/abs/2004.13486
AUTHORS:        Emine Yilmaz ; Nick Craswell ; Bhaskar Mitra ; Daniel Campos
HIGHLIGHT:      As deep learning based models are increasingly being used for information retrieval (IR), a major challenge is to ensure the availability of test collections for measuring their quality.

42, TITLE:      Finding Macro-Actions with Disentangled Effects for Efficient Planning with the Goal-Count Heuristic
http://arxiv.org/abs/2004.13242
AUTHORS:        Cameron Allen ; Tim Klinger ; George Konidaris ; Matthew Riemer ; Gerald Tesauro
COMMENTS:       Code available at https://github.com/camall3n/skills-for-planning
HIGHLIGHT:      We show experimentally that reducing entanglement exponentially decreases planning time with the goal-count heuristic.

43, TITLE:      HAPRec: Hybrid Activity and Plan Recognizer
http://arxiv.org/abs/2004.13482
AUTHORS:        Roger Granada ; Ramon Fraga Pereira ; Juarez Monteiro ; Leonardo Amado ; Rodrigo C. Barros ; Duncan Ruiz ; Felipe Meneguzzi
COMMENTS:       Demo paper of the AAAI 2020 Workshop on Plan, Activity, and Intent Recognition
HIGHLIGHT:      In this work, we demonstrate activity recognition in an indoor environment in order to identify the goal towards which the subject of the video is pursuing.

44, TITLE:      MultiMix: A Robust Data Augmentation Strategy for Cross-Lingual NLP
http://arxiv.org/abs/2004.13240
AUTHORS:        M Saiful Bari ; Muhammad Tasnim Mohiuddin ; Shafiq Joty
HIGHLIGHT:      In this work, we propose MultiMix, a novel data augmentation method for semi-supervised learning in zero-shot transfer learning scenarios.

45, TITLE:      Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition
http://arxiv.org/abs/2004.13236
AUTHORS:        Dung Nguyen ; Duc Thanh Nguyen ; Rui Zeng ; Thanh Thi Nguyen ; Son N. Tran ; Thin Nguyen ; Sridha Sridharan ; Clinton Fookes
COMMENTS:       Under Review on Transaction on Multimedia
HIGHLIGHT:      To address these challenges, in this paper, we propose a novel deep neural network architecture consisting of a two-stream auto-encoder and a long short term memory for effectively integrating visual and audio signal streams for emotion recognition.

46, TITLE:      Out-of-Sample Representation Learning for Multi-Relational Graphs
http://arxiv.org/abs/2004.13230
AUTHORS:        Marjan Albooyeh ; Rishab Goel ; Seyed Mehran Kazemi
HIGHLIGHT:      In this paper, we introduce the out-of-sample representation learning problem for non-attributed multi-relational graphs, create benchmark datasets for this task, develop several models and baselines, and provide empirical analyses and comparisons of the proposed models and baselines.

47, TITLE:      Linear Dependent Type Theory for Quantum Programming Languages
http://arxiv.org/abs/2004.13472
AUTHORS:        Peng Fu ; Kohei Kishida ; Peter Selinger
HIGHLIGHT:      This paper defines a general semantic structure for such a type theory via certain fibrations of monoidal categories.

48, TITLE:      Quantum Implications of Huang's Sensitivity Theorem
http://arxiv.org/abs/2004.13231
AUTHORS:        Scott Aaronson ; Shalev Ben-David ; Robin Kothari ; Avishay Tal
HIGHLIGHT:      Based on the recent breakthrough of Huang (2019), we show that for any total Boolean function $f$, the deterministic query complexity, $D(f)$, is at most quartic in the quantum query complexity, $Q(f)$: $D(f) = O(Q(f)^4)$.

49, TITLE:      FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net
http://arxiv.org/abs/2004.13470
AUTHORS:        Mina Jafari ; Ruizhe Li ; Yue Xing ; Dorothee Auer ; Susan Francis ; Jonathan Garibaldi ; Xin Chen
COMMENTS:       Accepted for publication at International Conference on Image and Graphics (ICIG 2019)
HIGHLIGHT:      In this paper, we present a generic deep convolutional neural network (DCNN) for multi-class image segmentation.

50, TITLE:      Attacks on Image Encryption Schemes for Privacy-Preserving Deep Neural Networks
http://arxiv.org/abs/2004.13263
AUTHORS:        Alex Habeen Chang ; Benjamin M. Case
COMMENTS:       For associated code, see https://github.com/ahchang98/image-encryption-scheme-attacks
HIGHLIGHT:      We present new chosen-plaintext and ciphertext-only attacks against both of these proposed image encryption schemes and demonstrate the attacks' effectiveness on several examples.

51, TITLE:      Learning Interpretable and Discrete Representations with Adversarial Training for Unsupervised Text Classification
http://arxiv.org/abs/2004.13255
AUTHORS:        Yau-Shian Wang ; Hung-Yi Lee ; Yun-Nung Chen
COMMENTS:       14 pages
HIGHLIGHT:      This work proposes TIGAN that learns to encode texts into two disentangled representations, including a discrete code and a continuous noise, where the discrete code represents interpretable topics, and the noise controls the variance within the topics.

52, TITLE:      Conversational Word Embedding for Retrieval-Based Dialog System
http://arxiv.org/abs/2004.13249
AUTHORS:        Wentao Ma ; Yiming Cui ; Ting Liu ; Dong Wang ; Shijin Wang ; Guoping Hu
COMMENTS:       To appear at ACL 2020
HIGHLIGHT:      In this paper, we propose a conversational word embedding method named PR-Embedding, which utilizes the conversation pairs $ \left\langle{post, reply} \right\rangle$ to learn word embedding.

53, TITLE:      $R^3$: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge
http://arxiv.org/abs/2004.13248
AUTHORS:        Tuhin Chakrabarty ; Debanjan Ghosh ; Smaranda Muresan ; Nanyun Peng
COMMENTS:       Accepted at the 2020 Annual Conference of the Association for Computational Linguistics (ACL)
HIGHLIGHT:      We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence.

54, TITLE:      Genetic programming approaches to learning fair classifiers
http://arxiv.org/abs/2004.13282
AUTHORS:        William La Cava ; Jason H. Moore
COMMENTS:       9 pages, 7 figures. GECCO 2020
HIGHLIGHT:      In this paper, current approaches to fairness are discussed and used to motivate algorithmic proposals that incorporate fairness into genetic programming for classification.

55, TITLE:      Iterative Variable Reordering: Taming Huge System Families
http://arxiv.org/abs/2004.13287
AUTHORS:        Clemens Dubslaff ; Andrey Morozov ; Christel Baier ; Klaus Janschek
COMMENTS:       In Proceedings MARS 2020, arXiv:2004.12403
HIGHLIGHT:      In this paper we describe a technique, called iterative variable reordering, that can enable the construction of large-scale family models.

56, TITLE:      Assessing the Bilingual Knowledge Learned by Neural Machine Translation Models
http://arxiv.org/abs/2004.13270
AUTHORS:        Shilin He ; Xing Wang ; Shuming Shi ; Michael R. Lyu ; Zhaopeng Tu
COMMENTS:       10 pages
HIGHLIGHT:      In this paper, we bridge the gap by assessing the bilingual knowledge learned by NMT models with phrase table -- an interpretable table of bilingual lexicons.

57, TITLE:      Trainable Activation Function Supported CNN in Image Classification
http://arxiv.org/abs/2004.13271
AUTHORS:        Zhaohe Liao
HIGHLIGHT:      In the current research of neural networks, the activation function is manually specified by human and not able to change themselves during training.

58, TITLE:      VD-BERT: A Unified Vision and Dialog Transformer with BERT
http://arxiv.org/abs/2004.13278
AUTHORS:        Yue Wang ; Shafiq Joty ; Michael R. Lyu ; Irwin King ; Caiming Xiong ; Steven C. H. Hoi
COMMENTS:       15 pages, 7 figures, 4 tables
HIGHLIGHT:      By contrast, in this work, we propose VD-BERT, a simple yet effective framework of unified vision-dialog Transformer that leverages the pretrained BERT language models for Visual Dialog tasks.

59, TITLE:      GIMP-ML: Python Plugins for using Computer Vision Models in GIMP
http://arxiv.org/abs/2004.13060
AUTHORS:        Kritik Soman
COMMENTS:       5 pages, 2 figures
HIGHLIGHT:      This paper introduces GIMP-ML, a set of Python plugins for the widely popular GNU Image Manipulation Program (GIMP).

60, TITLE:      Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners
http://arxiv.org/abs/2004.13291
AUTHORS:        Rodrigo Canaan ; Xianbo Gao ; Youjin Chung ; Julian Togelius ; Andy Nealen ; Stefan Menzel
HIGHLIGHT:      In this paper, we showthat agents trained through self-play using the popular RainbowDQN architecture fail to cooperate well with simple rule-basedagents that were not seen during training and, conversely, whenthese agents are trained to play with any individual rule-basedagent, or even a mix of these agents, they fail to achieve goodself-play scores.

61, TITLE:      Neural Hair Rendering
http://arxiv.org/abs/2004.13297
AUTHORS:        Menglei Chai ; Jian Ren ; Sergey Tulyakov
HIGHLIGHT:      In this paper, we propose a generic neural-based hair rendering pipeline that can synthesize photo-realistic images from virtual 3D hair models.

62, TITLE:      Addressing Artificial Intelligence Bias in Retinal Disease Diagnostics
http://arxiv.org/abs/2004.13515
AUTHORS:        Philippe Burlina ; Neil Joshi ; William Paul ; Katia D. Pacheco ; Neil M. Bressler
HIGHLIGHT:      This study evaluated novel AI and deep learning generative methods to address bias for retinal diagnostic applications when specifically applied to diabetic retinopathy (DR).

63, TITLE:      Small-Task Incremental Learning
http://arxiv.org/abs/2004.13513
AUTHORS:        Arthur Douillard ; Matthieu Cord ; Charles Ollion ; Thomas Robert ; Eduardo Valle
COMMENTS:       15 pages, 5 figures, 8 tables
HIGHLIGHT:      In this work, we propose PODNet, a model inspired by representation learning.

64, TITLE:      Learned Garbage Collection
http://arxiv.org/abs/2004.13301
AUTHORS:        Lujing Cen ; Ryan Marcus ; Hongzi Mao ; Justin Gottschlich ; Mohammad Alizadeh ; Tim Kraska
HIGHLIGHT:      In this preliminary work, we propose a design for a learned garbage collector that autonomously learns over time when to perform collections.

65, TITLE:      Spiking Machine Intelligence: What we can learn from biology and how spiking Neural Networks can help to improve Machine Learning
http://arxiv.org/abs/2004.13532
AUTHORS:        Richard C. Gerum ; Achim Schilling
HIGHLIGHT:      We show that biologically inspired neuron models such as the Integrate-and-Fire (LIF) neurons provide novel and efficient ways of information encoding.

66, TITLE:      Introducing a framework to assess newly created questions with Natural Language Processing
http://arxiv.org/abs/2004.13530
AUTHORS:        Luca Benedetto ; Andrea Cappelli ; Roberto Turrin ; Paolo Cremonesi
COMMENTS:       Accepted at the International Conference of Artificial Intelligence in Education
HIGHLIGHT:      In this paper, we propose a framework to train and evaluate models for estimating the difficulty and discrimination of newly created Multiple Choice Questions by extracting meaningful features from the text of the question and of the possible choices.

67, TITLE:      Augmented Behavioral Cloning from Observation
http://arxiv.org/abs/2004.13529
AUTHORS:        Juarez Monteiro ; Nathan Gavenski ; Roger Granada ; Felipe Meneguzzi ; Rodrigo Barros
COMMENTS:       This paper has been accepted in the International Joint Conference on Neural Networks 2020
HIGHLIGHT:      We address this problem with a novel approach that overcomes the problem of reaching bad local minima by exploring: (I) a self-attention mechanism that better captures global features of the states; and (ii) a sampling strategy that regulates the observations that are used for learning.

68, TITLE:      Let's be Humorous: Knowledge Enhanced Humor Generation
http://arxiv.org/abs/2004.13317
AUTHORS:        Hang Zhang ; Dayiheng Liu ; Jiancheng Lv ; Cheng Luo
COMMENTS:       6 pages, 4 figures, accepted by ACL2020 SRW
HIGHLIGHT:      In this paper, we explore how to generate a punchline given the set-up with the relevant knowledge. Furthermore, we create the first humor-knowledge dataset.

69, TITLE:      SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing
http://arxiv.org/abs/2004.13316
AUTHORS:        Xue Yang ; Junchi Yan ; Xiaokang Yang ; Jin Tang ; Wenlong Liao ; Tao He
COMMENTS:       15 pages, 12 figures, 10 tables
HIGHLIGHT:      SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing

70, TITLE:      Certifying Certainty and Uncertainty in Approximate Membership Query Structures -- Extended Version
http://arxiv.org/abs/2004.13312
AUTHORS:        Kiran Gopinathan ; Ilya Sergey
COMMENTS:       24 pages
HIGHLIGHT:      In this work, we address the challenge of building rigorous and reusable computer-assisted proofs about probabilistic specifications of AMQs.

71, TITLE:      Self-Attention with Cross-Lingual Position Representation
http://arxiv.org/abs/2004.13310
AUTHORS:        Liang Ding ; Longyue Wang ; Dacheng Tao
COMMENTS:       To appear in ACL 2020
HIGHLIGHT:      In this paper, we augment SANs with \emph{cross-lingual position representations} to model the bilingually aware latent structure for the input sentence.

72, TITLE:      Learning to Learn Morphological Inflection for Resource-Poor Languages
http://arxiv.org/abs/2004.13304
AUTHORS:        Katharina Kann ; Samuel R. Bowman ; Kyunghyun Cho
COMMENTS:       AAAI 2020
HIGHLIGHT:      We propose to cast the task of morphological inflection - mapping a lemma to an indicated inflected form - for resource-poor languages as a meta-learning problem.

73, TITLE:      Multivariate Confidence Calibration for Object Detection
http://arxiv.org/abs/2004.13546
AUTHORS:        Fabian Küppers ; Jan Kronenberger ; Amirhossein Shantia ; Anselm Haselhoff
COMMENTS:       Accepted on CVPR 2020 Workshop: "2nd Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD)"
HIGHLIGHT:      Therefore, we present a novel framework to measure and calibrate biased (or miscalibrated) confidence estimates of object detection methods.

74, TITLE:      Weakly Supervised POS Taggers Perform Poorly on Truly Low-Resource Languages
http://arxiv.org/abs/2004.13305
AUTHORS:        Katharina Kann ; Ophélie Lacroix ; Anders Søgaard
COMMENTS:       AAAI 2020
HIGHLIGHT:      We train and evaluate state-of-the-art weakly supervised POS taggers for a typologically diverse set of 15 truly low-resource languages.

75, TITLE:      Tree-depth and the Formula Complexity of Subgraph Isomorphism
http://arxiv.org/abs/2004.13302
AUTHORS:        Deepanshu Kush ; Benjamin Rossman
COMMENTS:       49 pages, 18 figures
HIGHLIGHT:      In this paper, we establish a close relationship between the $\textit{formula complexity}$ of $\mathrm{SUB}$ and an invariant known as $\textit{tree-depth}$ (denoted $\mathrm{td}(G)$).

76, TITLE:      Optimizing AI for Teamwork
http://arxiv.org/abs/2004.13102
AUTHORS:        Gagan Bansal ; Besmira Nushi ; Ece Kamar ; Eric Horvitz ; Daniel S. Weld
COMMENTS:       Pre-print/Draft
HIGHLIGHT:      So, we propose training AI systems in a human-centered manner and directly optimizing for team performance.

77, TITLE:      Do We Need Fully Connected Output Layers in Convolutional Networks?
http://arxiv.org/abs/2004.13587
AUTHORS:        Zhongchao Qian ; Tyler L. Hayes ; Kushal Kafle ; Christopher Kanan
HIGHLIGHT:      Do We Need Fully Connected Output Layers in Convolutional Networks?

78, TITLE:      Scheduled DropHead: A Regularization Method for Transformer Models
http://arxiv.org/abs/2004.13342
AUTHORS:        Wangchunshu Zhou ; Tao Ge ; Ke Xu ; Furu Wei ; Ming Zhou
HIGHLIGHT:      In this paper, we introduce DropHead, a structured dropout method specifically designed for regularizing the multi-head attention mechanism, which is a key component of transformer, a state-of-the-art model for various NLP tasks.

79, TITLE:      Embarrassingly Simple Unsupervised Aspect Extraction
http://arxiv.org/abs/2004.13580
AUTHORS:        Stéphan Tulkens ; Andreas van Cranenburgh
COMMENTS:       Accepted as ACL 2020 short paper
HIGHLIGHT:      We present a simple but effective method for aspect identification in sentiment analysis.

80, TITLE:      Semantics-Aware Inferential Network for Natural Language Understanding
http://arxiv.org/abs/2004.13338
AUTHORS:        Shuailiang Zhang ; Hai Zhao ; Junru Zhou
HIGHLIGHT:      Thus we propose a Semantics-Aware Inferential Network (SAIN) to meet such a motivation.

81, TITLE:      Unifying Neural Learning and Symbolic Reasoning for Spinal Medical Report Generation
http://arxiv.org/abs/2004.13577
AUTHORS:        Zhongyi Han ; Benzheng Wei ; Yilong Yin ; Shuo Li
COMMENTS:       Under review
HIGHLIGHT:      In this paper, we propose the neural-symbolic learning (NSL) framework that performs human-like learning by unifying deep neural learning and symbolic logical reasoning for the spinal medical report generation.

82, TITLE:      Learning Feature Descriptors using Camera Pose Supervision
http://arxiv.org/abs/2004.13324
AUTHORS:        Qianqian Wang ; Xiaowei Zhou ; Bharath Hariharan ; Noah Snavely
HIGHLIGHT:      In this paper we propose a novel weakly-supervised framework that can learn feature descriptors solely from relative camera poses between images.

83, TITLE:      Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes
http://arxiv.org/abs/2004.13567
AUTHORS:        Xin Yang ; Xu Wang ; Yi Wang ; Haoran Dou ; Shengli Li ; Huaxuan Wen ; Yi Lin ; Pheng-Ann Heng ; Dong Ni
COMMENTS:       Accepted by Computer Methods and Programs in Biomedicine
HIGHLIGHT:      In this paper, we propose the first fully-automated solution to segment the whole fetal head in US volumes.

84, TITLE:      Showing Your Work Doesn't Always Work
http://arxiv.org/abs/2004.13705
AUTHORS:        Raphael Tang ; Jaejun Lee ; Ji Xin ; Xinyu Liu ; Yaoliang Yu ; Jimmy Lin
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      In the present work, we critically examine this paper.

85, TITLE:      Autoencoding Word Representations through Time for Semantic Change Detection
http://arxiv.org/abs/2004.13703
AUTHORS:        Adam Tsakalidis ; Maria Liakata
HIGHLIGHT:      In this work, we propose three variants of sequential models for detecting semantically shifted words, effectively accounting for the changes in the word representations over time, in a temporally sensitive manner.

86, TITLE:      Entity Type Prediction in Knowledge Graphs using Embeddings
http://arxiv.org/abs/2004.13702
AUTHORS:        Russa Biswas ; Radina Sofronova ; Mehwish Alam ; Harald Sack
HIGHLIGHT:      To deal with this problem a multi-label classification approach is proposed in this work for entity typing using KG embeddings.

87, TITLE:      Word Interdependence Exposes How LSTMs Compose Representations
http://arxiv.org/abs/2004.13195
AUTHORS:        Naomi Saphra ; Adam Lopez
HIGHLIGHT:      For a closer look at how these representations are composed hierarchically, we present a novel measure of interdependence between word meanings in an LSTM, based on their interactions at the internal gates.

88, TITLE:      Gradient-Induced Co-Saliency Detection
http://arxiv.org/abs/2004.13364
AUTHORS:        Zhao Zhang ; Wenda Jin ; Jun Xu ; Ming-Ming Cheng
HIGHLIGHT:      In this paper, inspired by human behavior, we propose a gradient-induced co-saliency detection (GICD) method. To evaluate the performance of Co-SOD methods on discovering the co-salient object among multiple foregrounds, we construct a challenging CoCA dataset, where each image contains at least one extraneous foreground along with the co-salient object.

89, TITLE:      Conversational Question Answering over Passages by Leveraging Word Proximity Networks
http://arxiv.org/abs/2004.13117
AUTHORS:        Magdalena Kaiser ; Rishiraj Saha Roy ; Gerhard Weikum
COMMENTS:       SIGIR 2020 Demonstrations
HIGHLIGHT:      In this work, we demonstrate CROWN (Conversational passage ranking by Reasoning Over Word Networks): an unsupervised yet effective system for conversational QA with passage responses, that supports several modes of context propagation over multiple turns.

90, TITLE:      Transferable Active Grasping and Real Embodied Dataset
http://arxiv.org/abs/2004.13358
AUTHORS:        Xiangyu Chen ; Zelin Ye ; Jiankai Sun ; Yuda Fan ; Fang Hu ; Chenxi Wang ; Cewu Lu
HIGHLIGHT:      In our pipeline, we propose a novel mask-guided reward to overcome the sparse reward issue in grasping and ensure category-irrelevant behavior. To overcome the disadvantages of photo-realistic environment simulation, we propose a large-scale dataset called Real Embodied Dataset (RED), which includes full-viewpoint real samples on the upper hemisphere with amodal annotation and enables a simulator that has real visual feedback.

91, TITLE:      MAVEN: A Massive General Domain Event Detection Dataset
http://arxiv.org/abs/2004.13590
AUTHORS:        Xiaozhi Wang ; Ziqi Wang ; Xu Han ; Wangyi Jiang ; Rong Han ; Zhiyuan Liu ; Juanzi Li ; Peng Li ; Yankai Lin ; Jie Zhou
COMMENTS:       9 pages, 2 figures; Work in progress
HIGHLIGHT:      To alleviate these problems, we present a MAssive eVENt detection dataset (MAVEN), which contains 4,480 Wikipedia documents, 117,200 event mention instances, and 207 event types.

92, TITLE:      Multi-Scale Boosted Dehazing Network with Dense Feature Fusion
http://arxiv.org/abs/2004.13388
AUTHORS:        Hang Dong ; Jinshan Pan ; Lei Xiang ; Zhe Hu ; Xinyi Zhang ; Fei Wang ; Ming-Hsuan Yang
COMMENTS:       Accepted by CVPR 2020. The code are available at https://github.com/BookerDeWitt/MSBDN-DFF
HIGHLIGHT:      In this paper, we propose a Multi-Scale Boosted Dehazing Network with Dense Feature Fusion based on the U-Net architecture.

93, TITLE:      The Immersion of Directed Multi-graphs in Embedding Fields. Generalisations
http://arxiv.org/abs/2004.13384
AUTHORS:        Bogdan Bocse ; Ioan Radu Jinga
HIGHLIGHT:      The purpose of this paper is to outline a generalised model for representing hybrids of relational-categorical, symbolic, perceptual-sensory and perceptual-latent data, so as to embody, in the same architectural data layer, representations for the input, output and latent tensors.

94, TITLE:      Revisiting Multi-Task Learning in the Deep Learning Era
http://arxiv.org/abs/2004.13379
AUTHORS:        Simon Vandenhende ; Stamatios Georgoulis ; Marc Proesmans ; Dengxin Dai ; Luc Van Gool
COMMENTS:       A survey on recent multi-task learning techniques
HIGHLIGHT:      In this survey, we provide a well-rounded view on state-of-the-art MTL techniques within the context of deep neural networks.

95, TITLE:      3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis
http://arxiv.org/abs/2004.13371
AUTHORS:        Valentin Oreiller ;  Vincent~Andrearczyk ; Julien Fageot ; John O. Prior ; Adrien Depeursinge
COMMENTS:       10 pages
HIGHLIGHT:      In this work, we obtain LRI operators via the local projection of the image on the spherical harmonics basis, followed by the computation of the bispectrum, which shares and extends the invariance properties of the spectrum.

96, TITLE:      SSIM-Based CTU-Level Joint Optimal Bit Allocation and Rate Distortion Optimization
http://arxiv.org/abs/2004.13369
AUTHORS:        Yang Li ; Xuanqin Mou
HIGHLIGHT:      To solve this problem, we propose a $D_\text{SSIM}$-$D_\text{MSE}$ model first.

97, TITLE:      Clustering via torque balance with mass and distance
http://arxiv.org/abs/2004.13160
AUTHORS:        Jie Yang ; Chin-Teng Lin
COMMENTS:       28 pages, 12 figures, 7 tables
HIGHLIGHT:      Inspired by the torque balance that exists in gravitational interactions when galaxies merge, we propose a novel clustering method based on two natural properties of the universe: mass and distance.

98, TITLE:      PuzzLing Machines: A Challenge on Learning From Small Data
http://arxiv.org/abs/2004.13161
AUTHORS:        Gözde Gül Şahin ; Yova Kementchedjhieva ; Phillip Rust ; Iryna Gurevych
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      To expose this problem in a new light, we introduce a challenge on learning from small data, PuzzLing Machines, which consists of Rosetta Stone puzzles from Linguistic Olympiads for high school students.

99, TITLE:      Simultaneous Translation Policies: From Fixed to Adaptive
http://arxiv.org/abs/2004.13169
AUTHORS:        Baigong Zheng ; Kaibo Liu ; Renjie Zheng ; Mingbo Ma ; Hairong Liu ; Liang Huang
HIGHLIGHT:      We design an algorithm to achieve adaptive policies via a simple heuristic composition of a set of fixed policies.

100, TITLE:     A Disentangling Invertible Interpretation Network for Explaining Latent Representations
http://arxiv.org/abs/2004.13166
AUTHORS:        Patrick Esser ; Robin Rombach ; Björn Ommer
COMMENTS:       CVPR 2020. Project Page at https://compvis.github.io/iin/
HIGHLIGHT:      We formulate interpretation as a translation of hidden representations onto semantic concepts that are comprehensible to the user.

101, TITLE:     Would You Like Sashimi Even If It's Sliced Too Thin? Selective Neural Attention for Aspect Targeted Sentiment Analysis (SNAT)
http://arxiv.org/abs/2004.13150
AUTHORS:        Zhe Zhang ; Chung-Wei Hang ; Munindar P. Singh
HIGHLIGHT:      We propose SNAT, an approach that jointly considers aspects and targets when inferring sentiments.

102, TITLE:     EM-GAN: Fast Stress Analysis for Multi-Segment Interconnect Using Generative Adversarial Networks
http://arxiv.org/abs/2004.13181
AUTHORS:        Wentian Jin ; Sheriff Sadiqbatcha ; Jinwei Zhang ; Sheldon X. -D. Tan
HIGHLIGHT:      In this paper, we propose a fast transient hydrostatic stress analysis for electromigration (EM) failure assessment for multi-segment interconnects using generative adversarial networks (GANs).

103, TITLE:     Multi-Task Image-Based Dietary Assessment for Food Recognition and Portion Size Estimation
http://arxiv.org/abs/2004.13188
AUTHORS:        Jiangpeng He ; Zeman Shao ; Janine Wright ; Deborah Kerr ; Carol Boushey ; Fengqing Zhu
HIGHLIGHT:      In this work, we propose an end-to-end multi-task framework that can achieve both food classification and food portion size estimation. We introduce a food image dataset collected from a nutrition study where the groundtruth food portion is provided by registered dietitians.

104, TITLE:     A scoping review of transfer learning research on medical image analysis using ImageNet
http://arxiv.org/abs/2004.13175
AUTHORS:        Mohammad Amin Morid ; Alireza Borjali ; Guilherme Del Fiol
HIGHLIGHT:      We aimed to conduct a scoping review to identify these studies and summarize their characteristics in terms of the problem description, input, methodology, and outcome.

105, TITLE:     LSHR-Net: a hardware-friendly solution for high-resolution computational imaging using a mixed-weights neural network
http://arxiv.org/abs/2004.13173
AUTHORS:        Fangliang Bai ; Jinchao Liu ; Xiaojuan Liu ; Margarita Osadchy ; Chao Wang ; Stuart J. Gibson
HIGHLIGHT:      To address these problems, we propose a novel hardware-friendly solution based on mixed-weights neural networks for computational imaging.



==========Updates to Previous Papers==========
1, TITLE:       Random Error Sampling-based Recurrent Neural Network Architecture Optimization
http://arxiv.org/abs/1909.02425
AUTHORS:        Andrés Camero ; Jamal Toutouh ; Enrique Alba
HIGHLIGHT:      In this work, we introduce the Random Error Sampling-based Neuroevolution (RESN), an evolutionary algorithm that uses the mean absolute error random sampling, a training-free approach to predict the expected performance of an artificial neural network, to optimize the architecture of a network.

2, TITLE:       Endowing Empathetic Dialogue Systems with Personas
http://arxiv.org/abs/2004.12316
AUTHORS:        Peixiang Zhong ; Yan Zhu ; Yong Liu ; Chen Zhang ; Hao Wang ; Zaiqing Nie ; Chunyan Miao
HIGHLIGHT:      To this end, we propose a new task to endow empathetic dialogue systems with personas and present the first empirical study on the impacts of persona on empathetic responding. Specifically, we first present a novel large-scale multi-domain dataset for empathetic dialogues with personas.

3, TITLE:       CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification
http://arxiv.org/abs/1904.01308
AUTHORS:        Guillaume Delorme ; Yihong Xu ; Stephane Lathuilière ; Radu Horaud ; Xavier Alameda-Pineda
HIGHLIGHT:      In this paper, we propose to unify two trends in unsupervised person re-ID: clustering & fine-tuning and adversarial learning.

4, TITLE:       Instance-Invariant Adaptive Object Detection via Progressive Disentanglement
http://arxiv.org/abs/1911.08712
AUTHORS:        Aming Wu ; Yahong Han ; Linchao Zhu ; Yi Yang
COMMENTS:       The supporter of this paper requires us to temporarily withdraw the manuscript. Soon, it will be released again
HIGHLIGHT:      Particularly, base on disentangled learning used for feature decomposition, we devise two disentangled layers to decompose domain-invariant and domain-specific features.

5, TITLE:       Efficient Contraction of Large Tensor Networks for Weighted Model Counting through Graph Decompositions
http://arxiv.org/abs/1908.04381
AUTHORS:        Jeffrey M. Dudek ; Leonardo Dueñas-Osorio ; Moshe Y. Vardi
COMMENTS:       Submitted to AIJ
HIGHLIGHT:      In this work, we apply graph decompositions to find contraction orders for tensor networks.

6, TITLE:       DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders
http://arxiv.org/abs/1809.00977
AUTHORS:        Jacob Nogas ; Shehroz S. Khan ; Alex Mihailidis
HIGHLIGHT:      In this paper, we present a novel framework, \textit{DeepFall}, which formulates the fall detection problem as an anomaly detection problem.

7, TITLE:       Scaling MAP-Elites to Deep Neuroevolution
http://arxiv.org/abs/2003.01825
AUTHORS:        Cédric Colas ; Joost Huizinga ; Vashisht Madhavan ; Jeff Clune
COMMENTS:       Accepted to GECCO 2020
HIGHLIGHT:      In this paper, we propose to leverage the efficiency of Evolution Strategies (ES) to scale MAP-Elites to high-dimensional controllers parameterized by large neural networks.

8, TITLE:       A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time
http://arxiv.org/abs/1907.02731
AUTHORS:        Elena Burceanu ; Marius Leordeanu
COMMENTS:       accepted at International Joint Conference on Artificial Intelligence 2020 (IJCAI-2020)
HIGHLIGHT:      We formulate object segmentation in video as a graph partitioning problem in space and time, in which nodes are pixels and their relations form local neighborhoods.

9, TITLE:       TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing
http://arxiv.org/abs/2002.12620
AUTHORS:        Ziqing Yang ; Yiming Cui ; Zhipeng Chen ; Wanxiang Che ; Ting Liu ; Shijin Wang ; Guoping Hu
COMMENTS:       To appear at ACL 2020 Demo Session
HIGHLIGHT:      In this paper, we introduce TextBrewer, an open-source knowledge distillation toolkit designed for natural language processing.

10, TITLE:      Assessing Discourse Relations in Language Generation from Pre-trained Language Models
http://arxiv.org/abs/2004.12506
AUTHORS:        Wei-Jen Ko ; Junyi Jessy Li
HIGHLIGHT:      We propose a decoupled strategy to mitigate these problems and highlight the importance of explicitly modeling discourse information.

11, TITLE:      Keep It Real: a Window to Real Reality in Virtual Reality
http://arxiv.org/abs/2004.10313
AUTHORS:        Baihan Lin
COMMENTS:       IJCAI 2020
HIGHLIGHT:      This paper proposed a new interaction paradigm in the virtual reality (VR) environments, which consists of a virtual mirror or window projected onto a virtual surface, representing the correct perspective geometry of a mirror or window reflecting the real world.

12, TITLE:      Evolving Normalization-Activation Layers
http://arxiv.org/abs/2004.02967
AUTHORS:        Hanxiao Liu ; Andrew Brock ; Karen Simonyan ; Quoc V. Le
HIGHLIGHT:      Evolving Normalization-Activation Layers

13, TITLE:      RelationNet2: Deep Comparison Columns for Few-Shot Learning
http://arxiv.org/abs/1811.07100
AUTHORS:        Xueting Zhang ; Yuting Qiang ; Flood Sung ; Yongxin Yang ; Timothy M. Hospedales
COMMENTS:       10 pages, 5 figures, Published in IJCNN 2020
HIGHLIGHT:      Our insight is that effective general purpose matching requires non-linear comparison of features at multiple abstraction levels.

14, TITLE:      Improving BERT with Self-Supervised Attention
http://arxiv.org/abs/2004.03808
AUTHORS:        Xiaoyu Kou ; Yaming Yang ; Yujing Wang ; Ce Zhang ; Yiren Chen ; Yunhai Tong ; Yan Zhang ; Jing Bai
HIGHLIGHT:      In this paper, we propose a novel technique, called Self-Supervised Attention (SSA) to help facilitate this generalization challenge.

15, TITLE:      CURL: Contrastive Unsupervised Representations for Reinforcement Learning
http://arxiv.org/abs/2004.04136
AUTHORS:        Aravind Srinivas ; Michael Laskin ; Pieter Abbeel
COMMENTS:       First two authors contributed equally, website: https://mishalaskin.github.io/curl code: https://github.com/MishaLaskin/curl
HIGHLIGHT:      We present CURL: Contrastive Unsupervised Representations for Reinforcement Learning.

16, TITLE:      MaxiMin Active Learning in Overparameterized Model Classes}
http://arxiv.org/abs/1905.12782
AUTHORS:        Mina Karzand ; Robert D. Nowak
COMMENTS:       43 pages, 12 figures
HIGHLIGHT:      This paper proposes a new approach to active ML with nonparametric or overparameterized models such as kernel methods and neural networks.

17, TITLE:      Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation
http://arxiv.org/abs/2004.07672
AUTHORS:        Haoyu Song ; Yan Wang ; Wei-Nan Zhang ; Xiaojiang Liu ; Ting Liu
COMMENTS:       Accepted by ACL2020
HIGHLIGHT:      In this work, we introduce a three-stage framework that employs a generate-delete-rewrite mechanism to delete inconsistent words from a generated response prototype and further rewrite it to a personality-consistent one.

18, TITLE:      Deep Transfer Convolutional Neural Network and Extreme Learning Machine for Lung Nodule Diagnosis on CT images
http://arxiv.org/abs/2001.01279
AUTHORS:        Xufeng Huang ; Qiang Lei ; Tingli Xie ; Yahui Zhang ; Zhen Hu ; Qi Zhou
COMMENTS:       Some content of the article needs to be kept secret
HIGHLIGHT:      Deep Transfer Convolutional Neural Network and Extreme Learning Machine for Lung Nodule Diagnosis on CT images

19, TITLE:      A Survey on Domain Knowledge Powered Deep Learning for Medical Image Analysis
http://arxiv.org/abs/2004.12150
AUTHORS:        Xiaozheng Xie ; Jianwei Niu ; Xuefeng Liu ; Zhengsu Chen ; Shaojie Tang
COMMENTS:       26 pages, 13 figures
HIGHLIGHT:      In this survey, we summarize the current progress on introducing medical domain knowledge in deep learning models for various tasks like disease diagnosis, lesion, organ and abnormality detection, lesion and organ segmentation.

20, TITLE:      Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?
http://arxiv.org/abs/2004.03685
AUTHORS:        Alon Jacovi ; Yoav Goldberg
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      We call for more clearly differentiating between different desired criteria an interpretation should satisfy, and focus on the faithfulness criteria.

21, TITLE:      Predicting Landslides Using Contour Aligning Convolutional Neural Networks
http://arxiv.org/abs/1911.04651
AUTHORS:        Ainaz Hajimoradlou ; Gioachino Roberti ; David Poole
COMMENTS:       To be published in IJCAI2020
HIGHLIGHT:      We propose a model called Locally Aligned Convolutional Neural Network, LACNN, that follows the ground surface at multiple scales to predict possible landslide occurrence for a single point. To validate our method, we created a standardized dataset of georeferenced images consisting of the heterogeneous features as inputs, and compared our method to several baselines, including linear regression, a neural network, and a convolutional network, using log-likelihood error and Receiver Operating Characteristic curves on the test set.

22, TITLE:      Soft Threshold Weight Reparameterization for Learnable Sparsity
http://arxiv.org/abs/2002.03231
AUTHORS:        Aditya Kusupati ; Vivek Ramanujan ; Raghav Somani ; Mitchell Wortsman ; Prateek Jain ; Sham Kakade ; Ali Farhadi
COMMENTS:       18 pages, 10 figures
HIGHLIGHT:      This work proposes Soft Threshold Reparameterization (STR), a novel use of the soft-threshold operator on DNN weights.

23, TITLE:      Quantification of Tomographic Patterns associated with COVID-19 from Chest CT
http://arxiv.org/abs/2004.01279
AUTHORS:        Shikha Chaganti ; Abishek Balachandran ; Guillaume Chabin ; Stuart Cohen ; Thomas Flohr ; Bogdan Georgescu ; Philippe Grenier ; Sasa Grbic ; Siqi Liu ; François Mellot ; Nicolas Murray ; Savvas Nicolaou ; William Parker ; Thomas Re ; Pina Sanelli ; Alexander W. Sauter ; Zhoubing Xu ; Youngjin Yoo ; Valentin Ziebandt ; Dorin Comaniciu
HIGHLIGHT:      Purpose: To present a method that automatically detects and quantifies abnormal tomographic patterns commonly present in COVID-19, namely Ground Glass Opacities (GGO) and consolidations.

24, TITLE:      Signal Level Deep Metric Learning for Multimodal One-Shot Action Recognition
http://arxiv.org/abs/2004.11085
AUTHORS:        Raphael Memmesheimer ; Nick Theisen ; Dietrich Paulus
COMMENTS:       8 pages, 5figures, 6 tables
HIGHLIGHT:      Recognizing an activity with a single reference sample using metric learning approaches is a promising research field.

25, TITLE:      Learning to Update Natural Language Comments Based on Code Changes
http://arxiv.org/abs/2004.12169
AUTHORS:        Sheena Panthaplackel ; Pengyu Nie ; Milos Gligoric ; Junyi Jessy Li ; Raymond J. Mooney
COMMENTS:       Accepted in Association for Computational Linguistics (ACL) 2020
HIGHLIGHT:      We propose an approach that learns to correlate changes across two distinct language representations, to generate a sequence of edits that are applied to the existing comment to reflect the source code modifications.

26, TITLE:      ENIGMA Anonymous: Symbol-Independent Inference Guiding Machine (system description)
http://arxiv.org/abs/2002.05406
AUTHORS:        Jan Jakubův ; Karel Chvalovský ; Miroslav Olšák ; Bartosz Piotrowski ; Martin Suda ; Josef Urban
HIGHLIGHT:      We describe an implementation of gradient boosting and neural guidance of saturation-style automated theorem provers that does not depend on consistent symbol names across problems.

27, TITLE:      Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems
http://arxiv.org/abs/2003.01279
AUTHORS:        Nataniel Ruiz ; Sarah Adel Bargal ; Stan Sclaroff
COMMENTS:       Accepted at CVPR 2020 Workshop on Adversarial Machine Learning in Computer Vision
HIGHLIGHT:      We present a spread-spectrum adversarial attack, which evades blur defenses.

28, TITLE:      A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
http://arxiv.org/abs/1909.03227
AUTHORS:        Zhepei Wei ; Jianlin Su ; Yue Wang ; Yuan Tian ; Yi Chang
COMMENTS:       Accepted by ACL 2020. Code and data are available at: https://github.com/weizhepei/CasRel
HIGHLIGHT:      In this work, we introduce a fresh perspective to revisit the relational triple extraction task and propose a novel cascade binary tagging framework (CasRel) derived from a principled problem formulation.

29, TITLE:      How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence
http://arxiv.org/abs/2004.12158
AUTHORS:        Haoxi Zhong ; Chaojun Xiao ; Cunchao Tu ; Tianyang Zhang ; Zhiyuan Liu ; Maosong Sun
COMMENTS:       Accepted by ACL 2020
HIGHLIGHT:      In this paper, we introduce the history, the current state, and the future directions of research in LegalAI.

30, TITLE:      Semi-Supervised StyleGAN for Disentanglement Learning
http://arxiv.org/abs/2003.03461
AUTHORS:        Weili Nie ; Tero Karras ; Animesh Garg ; Shoubhik Debnath ; Anjul Patney ; Ankit B. Patel ; Anima Anandkumar
COMMENTS:       21 pages, 4 tables, 18 figures. Project page: https://sites.google.com/nvidia.com/semi-stylegan
HIGHLIGHT:      We propose new metrics to quantify generator controllability, and observe there may exist a crucial trade-off between disentangled representation learning and controllable generation. We create two complex high-resolution synthetic datasets for systematic testing.

31, TITLE:      Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization
http://arxiv.org/abs/2002.09809
AUTHORS:        Darvin Yi ; Endre Grøvik ; Michael Iv ; Elizabeth Tong ; Greg Zaharchuk ; Daniel Rubin
HIGHLIGHT:      We introduce a novel ensembling method, Random Bundle (RB), that improves performance for brain metastases segmentation.

32, TITLE:      OccuSeg: Occupancy-aware 3D Instance Segmentation
http://arxiv.org/abs/2003.06537
AUTHORS:        Lei Han ; Tian Zheng ; Lan Xu ; Lu Fang
COMMENTS:       CVPR 2020, video this https URL https://youtu.be/co7y6LQ7Kqc
HIGHLIGHT:      In this paper, we define "3D occupancy size", as the number of voxels occupied by each instance.

33, TITLE:      Meta-Learning without Memorization
http://arxiv.org/abs/1912.03820
AUTHORS:        Mingzhang Yin ; George Tucker ; Mingyuan Zhou ; Sergey Levine ; Chelsea Finn
COMMENTS:       ICLR 2020
HIGHLIGHT:      In this paper, we address this challenge by designing a meta-regularization objective using information theory that places precedence on data-driven adaptation.

34, TITLE:      Covering Codes for Insertions or Deletions
http://arxiv.org/abs/1911.09944
AUTHORS:        Andreas Lenz ; Cyrus Rashtchian ; Paul H. Siegel ; Eitan Yaakobi
COMMENTS:       13 pages
HIGHLIGHT:      We prove that codes exist with density that is only a factor $O(R \log R)$ larger than the lower bounds for all fixed~$R$.

35, TITLE:      LAMBERT: Layout-Aware (Language) Modeling using BERT for information extraction
http://arxiv.org/abs/2002.08087
AUTHORS:        Łukasz Garncarek ; Rafał Powalski ; Tomasz Stanisławek ; Bartosz Topolski ; Piotr Halama ; Filip Graliński
COMMENTS:       v1: 9 pages; work in progress; this version of the paper was submitted to review on Dec 10, 2019, and subsequently withdrawn on Feb 17, 2020 v2: 17 pages v3: 18 pages, 2 appendices
HIGHLIGHT:      In this paper we introduce a novel approach to the problem of understanding documents where the local semantics is influenced by non-trivial layout.

36, TITLE:      Pyramid Convolutional RNN for MRI Reconstruction
http://arxiv.org/abs/1912.00543
AUTHORS:        Puyang Wang ; Eric Z. Chen ; Terrence Chen ; Vishal M. Patel ; Shanhui Sun
HIGHLIGHT:      In this paper, we introduce a novel deep learning-based method, Pyramid Convolutional RNN (PC-RNN), to reconstruct the image from multiple scales.

37, TITLE:      Event-based Gesture Recognition with Dynamic Background Suppression using Smartphone Computational Capabilities
http://arxiv.org/abs/1811.07802
AUTHORS:        Jean-Matthieu Maro ; Ryad Benosman
COMMENTS:       Draft version; final version published in Frontiers in Neuroscience (open access)
HIGHLIGHT:      This paper introduces a framework of gesture recognition operating on the output of an event based camera using the computational resources of a mobile phone. We also introduce a new publicly available event-based dataset for gesture recognition selected through a clinical process to allow human-machine interactions for the visually-impaired and the elderly.

38, TITLE:      Alternative Function Approximation Parameterizations for Solving Games: An Analysis of $f$-Regression Counterfactual Regret Minimization
http://arxiv.org/abs/1912.02967
AUTHORS:        Ryan D'Orazio ; Dustin Morrill ; James R. Wright ; Michael Bowling
COMMENTS:       11 pages, includes appendix
HIGHLIGHT:      We derive approximation error-aware regret bounds for $(\Phi, f)$-regret matching, which applies to a general class of link functions and regret objectives.

39, TITLE:      How Much Knowledge Can You Pack Into the Parameters of a Language Model?
http://arxiv.org/abs/2002.08910
AUTHORS:        Adam Roberts ; Colin Raffel ; Noam Shazeer
COMMENTS:       Added results using "salient span masking" (Guu et al, 2020), achieving new state of the art on open domain WebQuestions and TriviaQA
HIGHLIGHT:      In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any external context or knowledge.

40, TITLE:      Grayscale Data Construction and Multi-Level Ranking Objective for Dialogue Response Selection
http://arxiv.org/abs/2004.02421
AUTHORS:        Zibo Lin ; Deng Cai ; Yan Wang ; Xiaojiang Liu ; Hai-Tao Zheng ; Shuming Shi
HIGHLIGHT:      We propose to automatically build training data with grayscale labels.

41, TITLE:      Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images
http://arxiv.org/abs/2004.05717
AUTHORS:        Eduardo Luz ; Pedro Lopes Silva ; Rodrigo Silva ; Ludmila Silva ; Gladston Moreira ; David Menotti
COMMENTS:       Copyright 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
HIGHLIGHT:      Thus, in this work, we propose to explore and extend the EfficientNet family of models using chest X-rays images to perform COVID-19 detection.

42, TITLE:      The Power of a Single Qubit: Two-way Quantum Finite Automata and the Word Problem
http://arxiv.org/abs/2003.09879
AUTHORS:        Zachary Remscrim
COMMENTS:       To appear in ICALP 2020
HIGHLIGHT:      As a further corollary, we show that 2QCFA can recognize certain non-context-free languages in expected polynomial time.

43, TITLE:      Pavement Image Datasets: A New Benchmark Dataset to Classify and Densify Pavement Distresses
http://arxiv.org/abs/1910.11123
AUTHORS:        Hamed Majidifard ; Peng Jin ; Yaw Adu-Gyamfi ; William G. Buttlar
HIGHLIGHT:      In this study, a labeled dataset approach is introduced as a first step towards a more robust, easy-to-deploy pavement condition assessment system.

44, TITLE:      Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning
http://arxiv.org/abs/1911.04283
AUTHORS:        Sathish Indurthi ; Houjeung Han ; Nikhil Kumar Lakumarapu ; Beomseok Lee ; Insoo Chung ; Sangha Kim ; Chanwoo Kim
COMMENTS:       ICASSP 2020
HIGHLIGHT:      In this work, we adopt a meta-learning algorithm to train a modality agnostic multi-task model that transfers knowledge from source tasks=ASR+MT to target task=ST where ST task severely lacks data.

45, TITLE:      A Local-to-Global Approach to Multi-modal Movie Scene Segmentation
http://arxiv.org/abs/2004.02678
AUTHORS:        Anyi Rao ; Linning Xu ; Yu Xiong ; Guodong Xu ; Qingqiu Huang ; Bolei Zhou ; Dahua Lin
COMMENTS:       CVPR2020. Project page: https://anyirao.com/projects/SceneSeg.html
HIGHLIGHT:      A Local-to-Global Approach to Multi-modal Movie Scene Segmentation

46, TITLE:      Predicting TUG score from gait characteristics with video analysis and machine learning
http://arxiv.org/abs/2003.00875
AUTHORS:        Jian Ma
COMMENTS:       Experimental results and discussion are revised. The code for estimating copula entropy is available at https://github.com/majianthu/copent
HIGHLIGHT:      In this paper, we propose a method for predicting TUG score from gait characteristics extracted from video with computer vision and machine learning technologies.

47, TITLE:      Logical Natural Language Generation from Open-Domain Tables
http://arxiv.org/abs/2004.10404
AUTHORS:        Wenhu Chen ; Jianshu Chen ; Yu Su ; Zhiyu Chen ; William Yang Wang
COMMENTS:       Accepted to ACL 2020 as Long Paper
HIGHLIGHT:      In this paper, we suggest a new NLG task where a model is tasked with generating natural language statements that can be \emph{logically entailed} by the facts in an open-domain semi-structured table.

48, TITLE:      Objects detection for remote sensing images based on polar coordinates
http://arxiv.org/abs/2001.02988
AUTHORS:        Lin Zhou ; Haoran Wei ; Hao Li ; Wenzhe Zhao ; Yi Zhang ; Yue Zhang
COMMENTS:       The paper needs a lot of revision. Some problem are not well described. However, this paper has spread out. I think the impact of an imperfect first draft is not good, so we want to withdraw and revise
HIGHLIGHT:      In this paper, we perform object detection in polar coordinates rather than in Cartesian coordinates, and propose a novel anchor-free detector for remote sensing images.

49, TITLE:      Contextual Neural Machine Translation Improves Translation of Cataphoric Pronouns
http://arxiv.org/abs/2004.09894
AUTHORS:        KayYen Wong ; Sameen Maruf ; Gholamreza Haffari
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      In this work, we investigate the effect of future sentences as context by comparing the performance of a contextual NMT model trained with the future context to the one trained with the past context.

50, TITLE:      Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems
http://arxiv.org/abs/2004.12846
AUTHORS:        Eseoghene Ben-Iwhiwhu ; Pawel Ladosz ; Jeffery Dick ; Wen-Hua Chen ; Praveen Pilly ; Andrea Soltoggio
COMMENTS:       9 pages. Accepted as a full paper in the Genetic and Evolutionary Computation Conference (GECCO 2020)
HIGHLIGHT:      In this paper, we exploit the highly adaptive nature of neuromodulated neural networks to evolve a controller that uses the latent space of an autoencoder in a POMDP.

51, TITLE:      Winning Isn't Everything: Enhancing Game Development with Intelligent Agents
http://arxiv.org/abs/1903.10545
AUTHORS:        Yunqi Zhao ; Igor Borovikov ; Fernando de Mesentier Silva ; Ahmad Beirami ; Jason Rupert ; Caedmon Somers ; Jesse Harder ; John Kolen ; Jervis Pinto ; Reza Pourabolghasem ; James Pestrak ; Harold Chaput ; Mohsen Sardari ; Long Lin ; Sundeep Narravula ; Navid Aghdaie ; Kazi Zaman
COMMENTS:       Accepted to IEEE Trans. Games
HIGHLIGHT:      In this paper, we study the problem of training intelligent agents in service of game development.

52, TITLE:      Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping -- Challenges and Opportunities
http://arxiv.org/abs/2002.08254
AUTHORS:        Michael Schmitt ; Jonathan Prexl ; Patrick Ebel ; Lukas Liebel ; Xiao Xiang Zhu
COMMENTS:       8 pages, 7 figures
HIGHLIGHT:      Therefore, this paper seeks to make a case for the application of weakly supervised learning strategies to get the most out of available data sources and achieve progress in high-resolution large-scale land cover mapping.

53, TITLE:      On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification
http://arxiv.org/abs/2004.12617
AUTHORS:        Xin Liu ; Jiefu Ou ; Yangqiu Song ; Xin Jiang
COMMENTS:       Accepted by IJCAI 2020
HIGHLIGHT:      We propose a novel model to combine these modules together.

54, TITLE:      DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
http://arxiv.org/abs/1911.00536
AUTHORS:        Yizhe Zhang ; Siqi Sun ; Michel Galley ; Yen-Chun Chen ; Chris Brockett ; Xiang Gao ; Jianfeng Gao ; Jingjing Liu ; Bill Dolan
COMMENTS:       Accepted by ACL 2020 system demonstration
HIGHLIGHT:      We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer).

55, TITLE:      Control Design of Autonomous Drone Using Deep Learning Based Image Understanding Techniques
http://arxiv.org/abs/2004.12886
AUTHORS:        Seid Miad Zandavi ; Vera Chung ; Ali Anaissi
HIGHLIGHT:      This paper presents a new framework to use images as the inputs for the controller to have autonomous flight, considering the noisy indoor environment and uncertainties.

56, TITLE:      Algebra-based Loop Synthesis
http://arxiv.org/abs/2004.11787
AUTHORS:        Andreas Humenberger ; Laura Kovács
HIGHLIGHT:      We present an algorithm for synthesizing program loops satisfying a given polynomial loop invariant.

57, TITLE:      GEVO: GPU Code Optimization using Evolutionary Computation
http://arxiv.org/abs/2004.08140
AUTHORS:        Jhe-Yu Liou ; Xiaodong Wang ; Stephanie Forrest ; Carole-Jean Wu
HIGHLIGHT:      We demonstrate that GEVO improves the execution time of the GPU programs in the Rodinia benchmark suite and the machine learning models, SVM and ResNet18, on NVIDIA Tesla P100.

58, TITLE:      Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification
http://arxiv.org/abs/1812.09617
AUTHORS:        Mozhi Zhang ; Yoshinari Fujinuma ; Jordan Boyd-Graber
COMMENTS:       AAAI 2020
HIGHLIGHT:      We present a cross-lingual document classification framework (CACO) that exploits cross-lingual subword similarity by jointly training a character-based embedder and a word-based classifier.

59, TITLE:      Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess Hypotheses
http://arxiv.org/abs/1911.03850
AUTHORS:        Erfan Sadeqi Azer ; Daniel Khashabi ; Ashish Sabharwal ; Dan Roth
COMMENTS:       ACL 2020
HIGHLIGHT:      Empirical research in Natural Language Processing (NLP) has adopted a narrow set of principles for assessing hypotheses, relying mainly on p-value computation, which suffers from several known issues.

60, TITLE:      Incremental Text-to-Speech Synthesis with Prefix-to-Prefix Framework
http://arxiv.org/abs/1911.02750
AUTHORS:        Mingbo Ma ; Baigong Zheng ; Kaibo Liu ; Renjie Zheng ; Hairong Liu ; Kainan Peng ; Kenneth Church ; Liang Huang
COMMENTS:       11 pages
HIGHLIGHT:      To reduce these latencies, we devise the first neural incremental TTS approach based on the recently proposed prefix-to-prefix framework.

61, TITLE:      Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS
http://arxiv.org/abs/2003.03972
AUTHORS:        Long Chen ; Haizhou Ai ; Rui Chen ; Zijie Zhuang ; Shuang Liu
COMMENTS:       12 pages with supplementary material; accepted to CVPR 2020
HIGHLIGHT:      In this paper, we present a novel solution for multi-human 3D pose estimation from multiple calibrated camera views. To further verify the scalability of our method, we propose a new large-scale multi-human dataset with 12 to 28 camera views.

62, TITLE:      Learning in Text Streams: Discovery and Disambiguation of Entity and Relation Instances
http://arxiv.org/abs/1909.05367
AUTHORS:        Marco Maggini ; Giuseppe Marra ; Stefano Melacci ; Andrea Zugarini
HIGHLIGHT:      We focus on the case in which individuals are entities and relations, and we propose an end-to-end trainable memory network that learns to discover and disambiguate them in an online manner, performing one-shot learning, and dealing with a small number of sparse supervisions.

63, TITLE:      On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
http://arxiv.org/abs/1909.03186
AUTHORS:        Sandeep Subramanian ; Raymond Li ; Jonathan Pilault ; Christopher Pal
HIGHLIGHT:      We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive summarization.

64, TITLE:      Utilising Low Complexity CNNs to Lift Non-Local Redundancies in Video Coding
http://arxiv.org/abs/1910.08737
AUTHORS:        Jan P. Klopp ; Liang-Gee Chen ; Shao-Yi Chien
COMMENTS:       13 pages, 3 figures
HIGHLIGHT:      In this work, we aim at exploiting non-local redundancies in video data that remain difficult to erase for conventional video codecs.

65, TITLE:      A general approach to progressive learning
http://arxiv.org/abs/2004.12908
AUTHORS:        Joshua T. Vogelstein ; Hayden S. Helm ; Ronak D. Mehta ; Jayanta Dey ; Weiwei Yang ; Bryan Tower ; Will LeVine ; Jonathan Larson ; Chris White ; Carey E. Priebe
HIGHLIGHT:      We propose a general approach to progressive learning that ensembles representations, rather than learners.

66, TITLE:      Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study
http://arxiv.org/abs/2004.11803
AUTHORS:        Larissa T. Triess ; David Peter ; Christoph B. Rist ; J. Marius Zöllner
COMMENTS:       Accepted at IEEE Intelligent Vehicles Symposium (IV) 2020. The code can be found here: http://ltriess.github.io/scan-semseg
HIGHLIGHT:      In this work, we perform a comprehensive experimental study of image-based semantic segmentation architectures for LiDAR point clouds. We propose a final set of the above methods with which the model achieves an increase of 3.2% in mIoU segmentation performance over the baseline while requiring only 42% of the original inference time.

67, TITLE:      There is Strength in Numbers: Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training
http://arxiv.org/abs/2004.07790
AUTHORS:        Joe Stacey ; Pasquale Minervini ; Haim Dubossarsky ; Sebastian Riedel ; Tim Rocktäschel
COMMENTS:       8 pages
HIGHLIGHT:      As a solution, we propose using an ensemble of adversaries during the training, encouraging the model to jointly decrease the accuracy of these different adversaries while fitting the data.

68, TITLE:      Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports
http://arxiv.org/abs/1911.02541
AUTHORS:        Yuhao Zhang ; Derek Merck ; Emily Bao Tsai ; Christopher D. Manning ; Curtis P. Langlotz
COMMENTS:       ACL2020. 13 pages with appendices
HIGHLIGHT:      In this work, we develop a general framework where we evaluate the factual correctness of a generated summary by fact-checking it automatically against its reference using an information extraction module.

69, TITLE:      Self-supervised Learning of Visual Speech Features with Audiovisual Speech Enhancement
http://arxiv.org/abs/2004.12031
AUTHORS:        Zakaria Aldeneh ; Anushree Prasanna Kumar ; Barry-John Theobald ; Erik Marchi ; Sachin Kajarekar ; Devang Naik ; Ahmed Hussen Abdelaziz
HIGHLIGHT:      We present an introspection of an audiovisual speech enhancement model.

70, TITLE:      Learning to Autofocus
http://arxiv.org/abs/2004.12260
AUTHORS:        Charles Herrmann ; Richard Strong Bowen ; Neal Wadhwa ; Rahul Garg ; Qiurui He ; Jonathan T. Barron ; Ramin Zabih
COMMENTS:       CVPR 2020
HIGHLIGHT:      We propose a learning-based approach to this problem, and provide a realistic dataset of sufficient size for effective learning.

71, TITLE:      The minimal probabilistic and quantum finite automata recognizing uncountably many languages with fixed cutpoints
http://arxiv.org/abs/1904.01381
AUTHORS:        Aleksejs Naumovs ; Maksims Dimitrijevs ; Abuzer Yakaryılmaz
COMMENTS:       12 pages, minor revisions, changing the format to "dmtcs-episciences" style
HIGHLIGHT:      In this note, we prove the same results for fixed cutpoints: each recognized language is associated with an automaton (i.e., algorithm), and the proofs use the fact that there are uncountably many automata.

72, TITLE:      Can AI help in screening Viral and COVID-19 pneumonia?
http://arxiv.org/abs/2003.13145
AUTHORS:        Muhammad E. H. Chowdhury ; Tawsifur Rahman ; Amith Khandakar ; Rashid Mazhar ; Muhammad Abdul Kadir ; Zaid Bin Mahbub ; Khandaker Reajul Islam ; Muhammad Salman Khan ; Atif Iqbal ; Nasser Al-Emadi ; Mamun Bin Ibne Reaz
COMMENTS:       11 pages, 10 Figures
HIGHLIGHT:      The aim of this paper is to automatically detect COVID-19 pneumonia patients using digital x-ray images while maximizing the accuracy in detection using image pre-processing and deep-learning techniques.

73, TITLE:      A simple way to reduce factorization problems to SAT
http://arxiv.org/abs/1708.02844
AUTHORS:        Davide Maran
COMMENTS:       Poorly written, not very relevant
HIGHLIGHT:      In this paper I show a simpler and more efficent method to reduce some factorization problems to the satisfability of a boolean formula.

74, TITLE:      Understanding Convolutional Neural Networks for Text Classification
http://arxiv.org/abs/1809.08037
AUTHORS:        Alon Jacovi ; Oren Sar Shalom ; Yoav Goldberg
COMMENTS:       Accepted to "Analyzing and interpreting neural networks for NLP" workshop in EMNLP 2018. v2: Added link to online github implementation
HIGHLIGHT:      We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text.

75, TITLE:      Regularizing CNN Transfer Learning with Randomised Regression
http://arxiv.org/abs/1908.05997
AUTHORS:        Yang Zhong ; Atsuto Maki
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      Unlike those related approaches, we suggest that an objective without a concrete goal can still serve well as a regularized.

76, TITLE:      DeepV2D: Video to Depth with Differentiable Structure from Motion
http://arxiv.org/abs/1812.04605
AUTHORS:        Zachary Teed ; Jia Deng
HIGHLIGHT:      We propose DeepV2D, an end-to-end deep learning architecture for predicting depth from video.