2020.02.19.txt

==========New Papers==========
1, TITLE:       Registration of multi-view point sets under the perspective of expectation-maximization
http://arxiv.org/abs/2002.07464
AUTHORS:        Jihua Zhu ; Jing Zhang ; Zhongyu Li
HIGHLIGHT:      To this end, this paper consider the multi-view registration problem as a maximum likelihood estimation problem and proposes a novel multi-view registration approach under the perspective of Expectation-Maximization (EM).

2, TITLE:       Towards Bounding-Box Free Panoptic Segmentation
http://arxiv.org/abs/2002.07705
AUTHORS:        Ujwal Bonde ; Pablo F. Alcantarilla ; Stefan Leutenegger
COMMENTS:       13 pages, 6 figures
HIGHLIGHT:      In this work we introduce a new bounding-box free network (BBFNet) for panoptic segmentation.

3, TITLE:       Voxel-Based Indoor Reconstruction From HoloLens Triangle Meshes
http://arxiv.org/abs/2002.07689
AUTHORS:        P. Hübner ; M. Weinmann ; S. Wursthorn
COMMENTS:       8 pages, 4 figures
HIGHLIGHT:      In this paper, we present a novel voxel-based approach for automated indoor reconstruction from unstructured three-dimensional geometries like triangle meshes.

4, TITLE:       Knowledge Integration Networks for Action Recognition
http://arxiv.org/abs/2002.07471
AUTHORS:        Shiwen Zhang ; Sheng Guo ; Limin Wang ; Weilin Huang ; Matthew R. Scott
COMMENTS:       To appear in AAAI 2020
HIGHLIGHT:      In this work, we propose Knowledge Integration Networks (referred as KINet) for video action recognition.

5, TITLE:       Few-Shot Few-Shot Learning and the role of Spatial Attention
http://arxiv.org/abs/2002.07522
AUTHORS:        Yann Lifchitz ; Yannis Avrithis ; Sylvaine Picard
HIGHLIGHT:      In doing so, we obtain from the pre-trained classifier a spatial attention map that allows focusing on objects and suppressing background clutter.

6, TITLE:       Computational optimization of convolutional neural networks using separated filters architecture
http://arxiv.org/abs/2002.07754
AUTHORS:        Elena Limonova ; Alexander Sheshkus ; Dmitry Nikolaev
COMMENTS:       4 pages, 3 figures
HIGHLIGHT:      In this paper we propose CNN structure transformation which expresses 2D convolution filters as a linear combination of separable filters.

7, TITLE:       FeatureNMS: Non-Maximum Suppression by Learning Feature Embeddings
http://arxiv.org/abs/2002.07662
AUTHORS:        Niels Ole Salscheider
HIGHLIGHT:      We propose FeatureNMS to solve this problem.

8, TITLE:       Computing the k Densest Subgraphs of a Graph
http://arxiv.org/abs/2002.07695
AUTHORS:        Riccardo Dondi ; Danny Hermelin
HIGHLIGHT:      In this paper we hope to remedy this situation by analyzing three natural variants of the k densest subgraphs problem.

9, TITLE:       Assessing the Memory Ability of Recurrent Neural Networks
http://arxiv.org/abs/2002.07422
AUTHORS:        Cheng Zhang ; Qiuchi Li ; Lingyu Hua ; Dawei Song
COMMENTS:       Accepted by ECAI 2020
HIGHLIGHT:      To tackle the problem, in this paper, we identify and analyze the internal and external factors that affect the memory ability of RNNs, and propose a Semantic Euclidean Space to represent the semantics expressed by a sequence.

10, TITLE:      Deflecting Adversarial Attacks
http://arxiv.org/abs/2002.07405
AUTHORS:        Yao Qin ; Nicholas Frosst ; Colin Raffel ; Garrison Cottrell ; Geoffrey Hinton
HIGHLIGHT:      We present a new approach towards ending this cycle where we "deflect'' adversarial attacks by causing the attacker to produce an input that semantically resembles the attack's target class.

11, TITLE:      A Model to Measure the Spread Power of Rumors
http://arxiv.org/abs/2002.07563
AUTHORS:        Zoleikha Jahanbakhsh-Nagadeh ; Mohammad-Reza Feizi-Derakhshi ; Majid Ramezani ; Taymaz Rahkar-Farshi ; Meysam Asgari-Chenaghlu ; Narjes Nikzad-Khasmakhi ; Ali-Reza Feizi-Derakhshi ; Mehrdad Ranjbar-Khadivi ; Elnaz Zafarani-Moattar ; Mohammad-Ali Balafar
COMMENTS:       38 pages, 9 tables, 5 figures
HIGHLIGHT:      This study investigates the problem of rumor analysis in different areas from other researches.

12, TITLE:      Spatial Concept-Based Navigation with Human Speech Instructions via Probabilistic Inference on Bayesian Generative Model
http://arxiv.org/abs/2002.07381
AUTHORS:        Akira Taniguchi ; Yoshinobu Hagiwara ; Tadahiro Taniguchi ; Tetsunari Inamura
COMMENTS:       Submitted
HIGHLIGHT:      The aim of this study is to enable a mobile robot to perform navigational tasks with human speech instructions, such as `Go to the kitchen', via probabilistic inference on a Bayesian generative model using spatial concepts.

13, TITLE:      Deep Learning in Medical Ultrasound Image Segmentation: a Review
http://arxiv.org/abs/2002.07703
AUTHORS:        Ziyang Wang
HIGHLIGHT:      In this review article, deep-learning-based methods for ultrasound image segmentation are categorized into six main groups according to their architectures and training at first.

14, TITLE:      Existence and Complexity of Approximate Equilibria in Weighted Congestion Games
http://arxiv.org/abs/2002.07466
AUTHORS:        George Christodoulou ; Martin Gairing ; Yiannis Giannakopoulos ; Diogo Poças ; Clara Waldmann
HIGHLIGHT:      We study the existence of approximate pure Nash equilibria ($\alpha$-PNE) in weighted atomic congestion games with polynomial cost functions of maximum degree $d$.

15, TITLE:      Default Ambiguity: Finding the Best Solution to the Clearing Problem
http://arxiv.org/abs/2002.07741
AUTHORS:        Pál András Papp ; Roger Wattenhofer
HIGHLIGHT:      In this paper, we study the general properties of the solution space of such financial systems, and analyze a wide range of reasonable objective functions for selecting from the set of solutions.

16, TITLE:      MAST: A Memory-Augmented Self-supervised Tracker
http://arxiv.org/abs/2002.07793
AUTHORS:        Zihang Lai ; Erika Lu ; Weidi Xie
COMMENTS:       In submission
HIGHLIGHT:      We propose a dense tracking model trained on videos without any annotations that surpasses previous self-supervised methods on existing benchmarks by a significant margin (+15%), and achieves performance comparable to supervised methods.

17, TITLE:      Optimal Error Pseudodistributions for Read-Once Branching Programs
http://arxiv.org/abs/2002.07208
AUTHORS:        Eshan Chattopadhyay ; Jyun-Jie Liao
HIGHLIGHT:      In this work, we construct a PRPD with seed length $$O(\log n\cdot \log (nw)\cdot \log\log(nw)+\log(1/\varepsilon)).

18, TITLE:      The Complexity of Interactively Learning a Stable Matching by Trial and Error
http://arxiv.org/abs/2002.07363
AUTHORS:        Ehsan Emamjomeh-Zadeh ; Yannai A. Gonczarowski ; David Kempe
HIGHLIGHT:      In a stable matching setting, we consider a query model that allows for an interactive learning algorithm to make precisely one type of query: proposing a matching, the response to which is either that the proposed matching is stable, or a blocking pair (chosen adversarially) indicating that this matching is unstable.

19, TITLE:      How incomputable is Kolmogorov complexity?
http://arxiv.org/abs/2002.07674
AUTHORS:        Paul Vitanyi ;  CWI ; University of Amsterdam
COMMENTS:       8 pages LaTeX
HIGHLIGHT:      We discuss the incomputabilty of Kolmogorov complexity, which formal loopholes this leaves us, recent approaches to compute or approximate Kolmogorov complexity, which approaches fail and which approaches are viable.

20, TITLE:      Generalized Neural Policies for Relational MDPs
http://arxiv.org/abs/2002.07375
AUTHORS:        Sankalp Garg ; Aniket Bajpai ;  Mausam
COMMENTS:       Under Review as a conference paper
HIGHLIGHT:      We present the first neural approach for solving RMDPs, expressed in the probabilistic planning language of RDDL.

21, TITLE:      Picking Winning Tickets Before Training by Preserving Gradient Flow
http://arxiv.org/abs/2002.07376
AUTHORS:        Chaoqi Wang ; Guodong Zhang ; Roger Grosse
COMMENTS:       Accepted at ICLR 2020
HIGHLIGHT:      We aim to prune networks at initialization, thereby saving resources at training time as well.

22, TITLE:      The Mathematical Structure of Integrated Information Theory
http://arxiv.org/abs/2002.07655
AUTHORS:        Johannes Kleiner ; Sean Tull
HIGHLIGHT:      In this contribution, we propound the mathematical structure of the theory, separating the essentials from auxiliary formal tools.

23, TITLE:      Time-Space Tradeoffs for Distinguishing Distributions and Applications to Security of Goldreich's PRG
http://arxiv.org/abs/2002.07235
AUTHORS:        Sumegha Garg ; Pravesh K. Kothari ; Ran Raz
COMMENTS:       35 pages
HIGHLIGHT:      In this work, we establish lower-bounds against memory bounded algorithms for distinguishing between natural pairs of related distributions from samples that arrive in a streaming setting.

24, TITLE:      Multistage s-t Path: Confronting Similarity with Dissimilarity
http://arxiv.org/abs/2002.07569
AUTHORS:        Till Fluschnik ; Rolf Niedermeier ; Carsten Schubert ; Philipp Zschoche
HIGHLIGHT:      Motivated by this fact and natural applications of this scenario e.g. in traffic route planning, we perform a parameterized complexity analysis.

25, TITLE:      N-fold integer programming via LP rounding
http://arxiv.org/abs/2002.07745
AUTHORS:        Jana Cslovjecsek ; Friedrich Eisenbrand ; Robert Weismantel
HIGHLIGHT:      In this paper we propose a different approach that is not based on augmentation.

26, TITLE:      From English To Foreign Languages: Transferring Pre-trained Language Models
http://arxiv.org/abs/2002.07306
AUTHORS:        Ke Tran
HIGHLIGHT:      In this work, we tackle the problem of transferring an existing pre-trained model from English to other languages under a limited computational budget.

27, TITLE:      Multiparty Karchmer-Wigderson Games and Threshold Circuits
http://arxiv.org/abs/2002.07444
AUTHORS:        Alexander Kozachinskiy ; Vladimir Podolskii
HIGHLIGHT:      We suggest a generalization of Karchmer-Wigderson communication games to the multiparty setting.

28, TITLE:      Identifying Critical Neurons in ANN Architectures using Mixed Integer Programming
http://arxiv.org/abs/2002.07259
AUTHORS:        Mostafa ElAraby ; Guy Wolf ; Margarida Carvalho
COMMENTS:       10 pages, 3 figures, 2 tables, under review
HIGHLIGHT:      We introduce a novel approach to optimize the architecture of deep neural networks by identifying critical neurons and removing non-critical ones.

29, TITLE:      Langevin DQN
http://arxiv.org/abs/2002.07282
AUTHORS:        Vikranth Dwaracherla ; Benjamin Van Roy
COMMENTS:       5 figures, 14 pages
HIGHLIGHT:      We answer this question in the affirmative.

30, TITLE:      Improving Multi-Turn Response Selection Models with Complementary Last-Utterance Selection by Instance Weighting
http://arxiv.org/abs/2002.07397
AUTHORS:        Kun Zhou ; Wayne Xin Zhao ; Yutao Zhu ; Ji-Rong Wen ; Jingsong Yu
COMMENTS:       12 pages. Accepted by PAKDD 2020
HIGHLIGHT:      To address this difficulty, we consider utilizing the underlying correlation in the data resource itself to derive different kinds of supervision signals and reduce the influence of noisy data.

31, TITLE:      Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue
http://arxiv.org/abs/2002.07510
AUTHORS:        Byeongchang Kim ; Jaewoo Ahn ; Gunhee Kim
COMMENTS:       Published in ICLR 2020 (Spotlight)
HIGHLIGHT:      As we focus on better modeling the knowledge selection in the multi-turn knowledge-grounded dialogue, we propose a sequential latent variable model as the first approach to this matter.

32, TITLE:      Conditional Self-Attention for Query-based Summarization
http://arxiv.org/abs/2002.07338
AUTHORS:        Yujia Xie ; Tianyi Zhou ; Yi Mao ; Weizhu Chen
HIGHLIGHT:      In this paper, we propose \textit{conditional self-attention} (CSA), a neural network module designed for conditional dependency modeling.

33, TITLE:      A New Clustering neural network for Chinese word segmentation
http://arxiv.org/abs/2002.07458
AUTHORS:        Yuze Zhao
HIGHLIGHT:      In this article I proposed a new model to achieve Chinese word segmentation(CWS),which may have the potentiality to apply in other domains in the future.It is a new thinking in CWS compared to previous works,to consider it as a clustering problem instead of a labeling problem.In this model,LSTM and self attention structures are used to collect context also sentence level features in every layer,and after several layers,a clustering model is applied to split characters into groups,which are the final segmentation results.I call this model CLNN.This algorithm can reach 98 percent of F score (without OOV words) and 85 percent to 95 percent F score (with OOV words) in training data sets.Error analyses shows that OOV words will greatly reduce performances,which needs a deeper research in the future.

34, TITLE:      Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims
http://arxiv.org/abs/2002.07725
AUTHORS:        Kevin Meng ; Damian Jimenez ; Fatma Arslan ; Jacob Daniel Devasier ; Daniel Obembe ; Chengkai Li
COMMENTS:       11 pages, 4 figures, 6 tables
HIGHLIGHT:      In the process, we propose a method to apply adversarial training to transformer models, which has the potential to be generalized to many similar text classification tasks. Along with our results, we are releasing our codebase and manually labeled datasets.

35, TITLE:      Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature
http://arxiv.org/abs/2002.07339
AUTHORS:        Fusataka Kuniyoshi ; Kohei Makino ; Jun Ozawa ; Makoto Miwa
COMMENTS:       Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020), Marseille, France
HIGHLIGHT:      In this work, we present a novel corpus of the synthesis process for all-solid-state batteries and an automated machine reading system for extracting the synthesis processes buried in the scientific literature. We define the representation of the synthesis processes using flow graphs, and create a corpus from the experimental sections of 243 papers.

36, TITLE:      Neural Relation Prediction for Simple Question Answering over Knowledge Graph
http://arxiv.org/abs/2002.07715
AUTHORS:        Amin Abolghasemi ; Saeedeh Momtazi
HIGHLIGHT:      In this paper, we propose an instance-based method to find similar questions of a new question, in the sense of their relations, to predict its mentioned relation.

37, TITLE:      A Survey of Deep Learning Techniques for Neural Machine Translation
http://arxiv.org/abs/2002.07526
AUTHORS:        Shuoheng Yang ; Yuxin Wang ; Xiaowen Chu
HIGHLIGHT:      A Survey of Deep Learning Techniques for Neural Machine Translation

38, TITLE:      3D Gated Recurrent Fusion for Semantic Scene Completion
http://arxiv.org/abs/2002.07269
AUTHORS:        Yu Liu ; Jie Li ; Qingsen Yan ; Xia Yuan ; Chunxia Zhao ; Ian Reid ; Cesar Cadena
COMMENTS:       13 pages
HIGHLIGHT:      We propose a 3D gated recurrent fusion network (GRFNet), which learns to adaptively select and fuse the relevant information from depth and RGB by making use of the gate and memory modules.

39, TITLE:      Distributed graph problems through an automata-theoretic lens
http://arxiv.org/abs/2002.07659
AUTHORS:        Yi-Jun Chang ; Jan Studený ; Jukka Suomela
HIGHLIGHT:      We study the following algorithm synthesis question: given the description of a locally checkable graph problem $\Pi$ for paths or cycles, determine in which instances $\Pi$ is solvable, determine what is the distributed round complexity of solving $\Pi$ in the usual $\mathsf{LOCAL}$ model of distributed computing, and construct an asymptotically optimal distributed algorithm for solving $\Pi$.

40, TITLE:      Robust Quantization: One Model to Rule Them All
http://arxiv.org/abs/2002.07686
AUTHORS:        Moran Shkolnik ; Brian Chmiel ; Ron Banner ; Gil Shomron ; Yuri Nahshan ; Alex Bronstein ; Uri Weiser
HIGHLIGHT:      To address this issue, we propose KURE, a method that provides intrinsic robustness to the model against a broad range of quantization implementations.

41, TITLE:      Evolutionary Optimization of Deep Learning Activation Functions
http://arxiv.org/abs/2002.07224
AUTHORS:        Garrett Bingham ; William Macke ; Risto Miikkulainen
COMMENTS:       8 pages; 9 figures/tables; submitted to GECCO 2020
HIGHLIGHT:      This paper shows that evolutionary algorithms can discover novel activation functions that outperform ReLU.

42, TITLE:      An enhanced Tree-LSTM architecture for sentence semantic modeling using typed dependencies
http://arxiv.org/abs/2002.07775
AUTHORS:        Jeena Kleenankandy ; K. A. Abdul Nazeer
COMMENTS:       This is a preprint submitted to Journal of Information Processing and Management ( Elsevier ) on December 29, 2019
HIGHLIGHT:      This paper proposes an enhanced LSTM architecture, called relation gated LSTM, which can model the relationship between two inputs of a sequence using a control input.

43, TITLE:      AdaEnsemble Learning Approach for Metro Passenger Flow Forecasting
http://arxiv.org/abs/2002.07575
AUTHORS:        Shaolong Sun ; Dongchuan Yang ; Gengzhong Feng ; Ju-e Guo
HIGHLIGHT:      In this study, we present a novel adaptive ensemble (AdaEnsemble) learning approach to accurately forecast the volume of metro passenger flows, and it combines the complementary advantages of variational mode decomposition (VMD), seasonal autoregressive integrated moving averaging (SARIMA), multilayer perceptron network (MLP) and long short-term memory (LSTM) network.

44, TITLE:      KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge
http://arxiv.org/abs/2002.07418
AUTHORS:        Peng Zhang ; Jianye Hao ; Weixun Wang ; Hongyao Tang ; Yi Ma ; Yihai Duan ; Yan Zheng
HIGHLIGHT:      Taking this inspiration, we propose knowledge guided policy network (KoGuN), a novel framework that combines human prior suboptimal knowledge with reinforcement learning.

45, TITLE:      MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding
http://arxiv.org/abs/2002.07408
AUTHORS:        Chaoqi Yang ; Junwei Lu ; Xiaofeng Gao ; Haishan Liu ; Qiong Chen ; Gongshen Liu ; Guihai Chen
COMMENTS:       8 Pages, Extensive Experiments
HIGHLIGHT:      To address the problem, we propose a multi-objective reinforcement learning algorithm, named MoTiAC, for the problem of bidding optimization with various goals.

46, TITLE:      Learning by Semantic Similarity Makes Abstractive Summarization Better
http://arxiv.org/abs/2002.07767
AUTHORS:        Wonjin Yoon ; Yoon Sun Yeo ; Minbyul Jeong ; Bong-Jun Yi ; Jaewoo Kang
HIGHLIGHT:      In this paper, we propose Semantic Similarity strategy that can consider semantic meanings of generated summaries while training.

47, TITLE:      An Overview of Distance and Similarity Functions for Structured Data
http://arxiv.org/abs/2002.07420
AUTHORS:        Santiago Ontañón
HIGHLIGHT:      Therefore, the goal of this paper is to provide an overview of this work to identify connections between the work carried out in different areas and point out directions for future work.

48, TITLE:      A Scalable Method for Scheduling Distributed Energy Resources using Parallelized Population-based Metaheuristics
http://arxiv.org/abs/2002.07505
AUTHORS:        Hatem Khalloof ; Wilfried Jakob ; Shadi Shahoud ; Clemens Duepmeier ; Veit Hagenmeyer
HIGHLIGHT:      In the present paper, a new generic and highly scalable parallel method for unit commitment of distributed energy resources using metaheuristic algorithms is presented.

49, TITLE:      A Computationally Efficient Neural Network Invariant to the Action of Symmetry Subgroups
http://arxiv.org/abs/2002.07528
AUTHORS:        Piotr Kicki ; Mete Ozay ; Piotr Skrzypczyński
HIGHLIGHT:      We introduce a method to design a computationally efficient $G$-invariant neural network that approximates functions invariant to the action of a given permutation subgroup $G \leq S_n$ of the symmetric group on input data.

50, TITLE:      Balancing reconstruction error and Kullback-Leibler divergence in Variational Autoencoders
http://arxiv.org/abs/2002.07514
AUTHORS:        Andrea Asperti ; Matteo Trentin
HIGHLIGHT:      In this article, we show that learning can be replaced by a simple deterministic computation, helping to understand the underlying mechanism, and resulting in a faster and more accurate behaviour.

51, TITLE:      Decision-Making with Auto-Encoding Variational Bayes
http://arxiv.org/abs/2002.07217
AUTHORS:        Romain Lopez ; Pierre Boyeau ; Nir Yosef ; Michael I. Jordan ; Jeffrey Regier
HIGHLIGHT:      Motivated by these theoretical results, we propose a novel variant of the VAE.

52, TITLE:      EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement
http://arxiv.org/abs/2002.07421
AUTHORS:        Linpu Fang ; Hang Xu ; Zhili Liu ; Sarah Parisot ; Zhenguo Li
COMMENTS:       Accepted by AAAI20
HIGHLIGHT:      In this paper, we study the hybrid-supervised object detection problem, aiming to train a high quality detector with only a limited amount of fullyannotated data and fully exploiting cheap data with imagelevel labels.

53, TITLE:      Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN
http://arxiv.org/abs/2002.07417
AUTHORS:        Hang Xu ; Linpu Fang ; Xiaodan Liang ; Wenxiong Kang ; Zhenguo Li
COMMENTS:       Accepted by AAAI20
HIGHLIGHT:      In this paper, we address the problem of designing a universal object detection model that exploits diverse category granularity from multiple domains and predict all kinds of categories in one system.

54, TITLE:      Constraining Temporal Relationship for Action Localization
http://arxiv.org/abs/2002.07358
AUTHORS:        Peisen Zhao ; Lingxi Xie ; Chen Ju ; Ya Zhang ; Qi Tian
HIGHLIGHT:      This paper delves deep into this mechanism, and argues that existing approaches mostly ignored the potential relationship of these curves, and results in low quality of action proposals.

55, TITLE:      DivideMix: Learning with Noisy Labels as Semi-supervised Learning
http://arxiv.org/abs/2002.07394
AUTHORS:        Junnan Li ; Richard Socher ; Steven C. H. Hoi
HIGHLIGHT:      In this work, we propose DivideMix, a novel framework for learning with noisy labels by leveraging semi-supervised learning techniques.

56, TITLE:      Multi-Task Learning from Videos via Efficient Inter-Frame Attention
http://arxiv.org/abs/2002.07362
AUTHORS:        Donghyun Kim ; Tian Lan ; Chuhang Zou ; Ning Xu ; Bryan A. Plummer ; Stan Sclaroff ; Jayan Eledath ; Gerard Medioni
HIGHLIGHT:      In this work, we present a new approach for multi-task learning from videos.

57, TITLE:      High-Order Paired-ASPP Networks for Semantic Segmenation
http://arxiv.org/abs/2002.07371
AUTHORS:        Yu Zhang ; Xin Sun ; Junyu Dong ; Changrui Chen ; Yue Shen
HIGHLIGHT:      In this paper, we propose High-Order Paired-ASPP Network to exploit high-order statistics from various feature levels.

58, TITLE:      V4D:4D Convolutional Neural Networks for Video-level Representation Learning
http://arxiv.org/abs/2002.07442
AUTHORS:        Shiwen Zhang ; Sheng Guo ; Weilin Huang ; Matthew R. Scott ; Limin Wang
COMMENTS:       To appear in ICLR2020
HIGHLIGHT:      In this paper, we propose Video-level 4D Convolutional Neural Networks, referred as V4D, to model the evolution of long-range spatio-temporal representation with 4D convolutions, and at the same time, to preserve strong 3D spatio-temporal representation with residual connections.

59, TITLE:      The Tree Ensemble Layer: Differentiability meets Conditional Computation
http://arxiv.org/abs/2002.07772
AUTHORS:        Hussein Hazimeh ; Natalia Ponomareva ; Petros Mol ; Zhenyu Tan ; Rahul Mazumder
HIGHLIGHT:      We aim to combine these advantages by introducing a new layer for neural networks, composed of an ensemble of differentiable decision trees (a.k.a. soft trees).

60, TITLE:      Learning Bijective Feature Maps for Linear ICA
http://arxiv.org/abs/2002.07766
AUTHORS:        Alexander Camuto ; Matthew Willetts ; Brooks Paige ; Chris Holmes ; Stephen Roberts
COMMENTS:       8 pages
HIGHLIGHT:      Here we develop a method that jointly learns a linear independent component analysis (ICA) model with non-linear bijective feature maps.

61, TITLE:      MapLUR: Exploring a new Paradigm for Estimating Air Pollution using Deep Learning on Map Images
http://arxiv.org/abs/2002.07493
AUTHORS:        Michael Steininger ; Konstantin Kobs ; Albin Zehe ; Florian Lautenschlager ; Martin Becker ; Andreas Hotho
COMMENTS:       Accepted for publication in ACM TSAS - Special Issue on Deep Learning
HIGHLIGHT:      In this paper, we advocate a paradigm shift for LUR models: We propose the Data-driven, Open, Global (DOG) paradigm that entails models based on purely data-driven approaches using only openly and globally available data.

62, TITLE:      Machine Learning for Motor Learning: EEG-based Continuous Assessment of Cognitive Engagement for Adaptive Rehabilitation Robots
http://arxiv.org/abs/2002.07541
AUTHORS:        Neelesh Kumar ; Konstantinos P. Michmizos
COMMENTS:       6 pages, 6 figures, 1 table
HIGHLIGHT:      Here, we propose an end-to-end computational framework that assesses CE in real-time, using electroencephalography (EEG) signals as objective measurements.

63, TITLE:      A Spiking Neural Network Emulating the Structure of the Oculomotor System Requires No Learning to Control a Biomimetic Robotic Head
http://arxiv.org/abs/2002.07534
AUTHORS:        Praveenram Balachandar ; Konstantinos P. Michmizos
COMMENTS:       6 pages, 3 figures
HIGHLIGHT:      Here, we report the tracking performance of the robotic head and show that the robotic eye kinematics are similar to those reported in human eye studies.

64, TITLE:      Denotational semantics as a foundation for cost recurrence extraction for functional languages
http://arxiv.org/abs/2002.07262
AUTHORS:        Norman Danner ; Daniel R. Licata
HIGHLIGHT:      The key feature of this second phase is that different models describe different notions of size.

65, TITLE:      ConSORT: Context- and Flow-Sensitive Ownership Refinement Types for Imperative Programs
http://arxiv.org/abs/2002.07770
AUTHORS:        John Toman ; Ren Siqi ; Kohei Suenaga ; Atsushi Igarashi ; Naoki Kobayashi
HIGHLIGHT:      We present ConSORT, a type system for safety verification in the presence of mutability and aliasing.

66, TITLE:      Managing multiple data streams in R
http://arxiv.org/abs/2002.07472
AUTHORS:        Mark P. J. van der Loo
COMMENTS:       16 pages, 1 figure. Submitted to the R Journal (2019-08-22)
HIGHLIGHT:      In this paper we demonstrate an approach that abstracts collection and processing of such secondary information from the code in the running script.

67, TITLE:      Dual-Attention GAN for Large-Pose Face Frontalization
http://arxiv.org/abs/2002.07227
AUTHORS:        Yu Yin ; Songyao Jiang ; Joseph P. Robinson ; Yun Fu
COMMENTS:       The 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)
HIGHLIGHT:      In this paper, we present a novel Dual-Attention Generative Adversarial Network (DA-GAN) for photo-realistic face frontalization by capturing both contextual dependencies and local consistency during GAN training.

68, TITLE:      Multilinear Compressive Learning with Prior Knowledge
http://arxiv.org/abs/2002.07203
AUTHORS:        Dat Thanh Tran ; Moncef Gabbouj ; Alexandros Iosifidis
COMMENTS:       15 pages, 1 figure, 7 tables
HIGHLIGHT:      In this paper, we propose a novel solution to address both of the aforementioned requirements, i.e., How to find those tensor subspaces in which the signals of interest are highly separable?

69, TITLE:      Decidability of cutpoint isolation for letter-monotonic probabilistic finite automata
http://arxiv.org/abs/2002.07660
AUTHORS:        Paul C. Bell ; Pavel Semukhin
HIGHLIGHT:      We show the surprising result that the cutpoint isolation problem is decidable for probabilistic finite automata where input words are taken from a letter-monotonic context-free language.

70, TITLE:      Uncertainty in Structured Prediction
http://arxiv.org/abs/2002.07650
AUTHORS:        Andrey Malinin ; Mark Gales
HIGHLIGHT:      Thus, this work aims to investigate uncertainty estimation for structured prediction tasks within a single unified and interpretable probabilistic ensemble-based framework.



==========Updates to Previous Papers==========
1, TITLE:       Groups with ALOGTIME-hard word problems and PSPACE-complete compressed word problems
http://arxiv.org/abs/1909.13781
AUTHORS:        Laurent Bartholdi ; Michael Figelius ; Markus Lohrey ; Armin Weiß
HIGHLIGHT:      We give lower bounds on the complexity of the word problem of certain non-solvable groups: for a large class of non-solvable infinite groups, including in particular free groups, Grigorchuk's group and Thompson's groups, we prove that their word problem is $\mathsf{NC}^1$-hard.

2, TITLE:       Continual egocentric object recognition
http://arxiv.org/abs/1912.05029
AUTHORS:        Luca Erculiani ; Fausto Giunchiglia ; Andrea Passerini
HIGHLIGHT:      We present a framework capable of tackilng the problem of continual object recognition in a setting which resembles that under whichhumans see and learn.

3, TITLE:       Shallow Water Bathymetry Mapping from UAV Imagery based on Machine Learning
http://arxiv.org/abs/1902.10733
AUTHORS:        Panagiotis Agrafiotis ; Dimitrios Skarlatos ; Andreas Georgopoulos ; Konstantinos Karantzalos
COMMENTS:       8 pages, 9 figures
HIGHLIGHT:      In this paper, in order to overcome the water refraction errors, we employ machine learning tools that are able to learn the systematic underestimation of the estimated depths.

4, TITLE:       Meta-Learning with Warped Gradient Descent
http://arxiv.org/abs/1909.00025
AUTHORS:        Sebastian Flennerhag ; Andrei A. Rusu ; Razvan Pascanu ; Francesco Visin ; Hujun Yin ; Raia Hadsell
COMMENTS:       28 pages, 13 figures, 3 tables. Published as a conference paper at ICLR 2020
HIGHLIGHT:      In this work, we propose Warped Gradient Descent (WarpGrad), a method that intersects these approaches to mitigate their limitations.

5, TITLE:       PCGRL: Procedural Content Generation via Reinforcement Learning
http://arxiv.org/abs/2001.09212
AUTHORS:        Ahmed Khalifa ; Philip Bontrager ; Sam Earle ; Julian Togelius
COMMENTS:       7 pages, 7 figures, 1 table, submitted to IJCAI conference
HIGHLIGHT:      We investigate three different ways of transforming two-dimensional level design problems into Markov decision processes and apply these to three game environments.

6, TITLE:       Audio-attention discriminative language model for ASR rescoring
http://arxiv.org/abs/1912.03363
AUTHORS:        Ankur Gandhe ; Ariya Rastrow
COMMENTS:       4 pages, 1 figure, Accepted at ICASSP 2020
HIGHLIGHT:      In this work, we propose to combine the benefits of end-to-end approaches with a conventional system using an attention-based discriminative language model that learns to rescore the output of a first-pass ASR system.

7, TITLE:       Total Deep Variation for Linear Inverse Problems
http://arxiv.org/abs/2001.05005
AUTHORS:        Erich Kobler ; Alexander Effland ; Karl Kunisch ; Thomas Pock
COMMENTS:       21 pages, 10 figures
HIGHLIGHT:      In this paper, we propose a novel learnable general-purpose regularizer exploiting recent architectural design patterns from deep learning.

8, TITLE:       Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
http://arxiv.org/abs/2002.06604
AUTHORS:        Yeongmin Ko ; Jiwon Jun ; Donghwuy Ko ; Moongu Jeon
HIGHLIGHT:      In this paper, we propose a novel lane detection method for the arbitrary number of lanes using the deep learning method, which has the lower number of false positives than other recent lane detection methods.

9, TITLE:       A survey of parameterized algorithms and the complexity of edge modification
http://arxiv.org/abs/2001.06867
AUTHORS:        Christophe Crespelle ; Pål Grønås Drange ; Fedor V. Fomin ; Petr A. Golovach
COMMENTS:       Incorporated comments from Marcin Pilipczuk, William Lochet, and Dekel Tsur
HIGHLIGHT:      The survey provides an overview of the developing area of parameterized algorithms for graph modification problems.

10, TITLE:      Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions
http://arxiv.org/abs/1907.02957
AUTHORS:        Yao Qin ; Nicholas Frosst ; Sara Sabour ; Colin Raffel ; Garrison Cottrell ; Geoffrey Hinton
HIGHLIGHT:      To specifically attack our detection mechanism, we propose the Reconstructive Attack which seeks both to cause a misclassification and a low reconstruction error.

11, TITLE:      PitchNet: Unsupervised Singing Voice Conversion with Pitch Adversarial Network
http://arxiv.org/abs/1912.01852
AUTHORS:        Chengqi Deng ; Chengzhu Yu ; Heng Lu ; Chao Weng ; Dong Yu
COMMENTS:       Accepted by ICASSP 2020
HIGHLIGHT:      In this paper, we propose to advance the existing unsupervised singing voice conversion method proposed in [1] to achieve more accurate pitch translation and flexible pitch manipulation.

12, TITLE:      Human Gait Database for Normal Walk Collected by Smart Phone Accelerometer
http://arxiv.org/abs/1905.03109
AUTHORS:        Amir Vajdi ; Mohammad Reza Zaghian ; Saman Farahmand ; Elham Rastegar ; Kian Maroofi ; Shaohua Jia ; Marc Pomplun ; Nurit Haspel ; Akram Bayat
HIGHLIGHT:      The goal of this study is to introduce a comprehensive gait database of 93 human subjects who walked between two endpoints during two different sessions and record their gait data using two smartphones, one was attached to the right thigh and another one on the left side of the waist.

13, TITLE:      Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework
http://arxiv.org/abs/1910.04708
AUTHORS:        Zirui Wang ; Jiateng Xie ; Ruochen Xu ; Yiming Yang ; Graham Neubig ; Jaime Carbonell
COMMENTS:       Published as a conference paper at ICLR 2020. First two authors contributed equally. Source code is available at https://github.com/thespectrewithin/joint-align
HIGHLIGHT:      Stemming from this analysis, we propose a simple and novel framework that combines these two previously mutually-exclusive approaches.

14, TITLE:      Complexity of Stability
http://arxiv.org/abs/1910.00305
AUTHORS:        Fabian Frei ; Edith Hemaspaandra ; Jörg Rothe
HIGHLIGHT:      We initiate the study of stability of graphs for such parameters in terms of their computational complexity.

15, TITLE:      Decision list compression by mild random restrictions
http://arxiv.org/abs/1909.10658
AUTHORS:        Shachar Lovett ; Kewen Wu ; Jiapeng Zhang
COMMENTS:       16 pages
HIGHLIGHT:      We prove that decision lists of small width can always be approximated by decision lists of small size, where we obtain sharp bounds.

16, TITLE:      The Complexity of Verifying Loop-free Programs as Differentially Private
http://arxiv.org/abs/1911.03272
AUTHORS:        Marco Gaboardi ; Kobbi Nissim ; David Purser
HIGHLIGHT:      We study the problem of verifying differential privacy for loop-free programs with probabilistic choice.

17, TITLE:      Neural Machine Translation with Joint Representation
http://arxiv.org/abs/2002.06546
AUTHORS:        Yanyang Li ; Qiang Wang ; Tong Xiao ; Tongran Liu ; Jingbo Zhu
COMMENTS:       AAAI 2020
HIGHLIGHT:      In this paper, we employ Joint Representation that fully accounts for each possible interaction.

18, TITLE:      Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification
http://arxiv.org/abs/2002.04114
AUTHORS:        Guan-An Wang ; Tianzhu Zhang. Yang Yang ; Jian Cheng ; Jianlong Chang ; Xu Liang ; Zengguang Hou
COMMENTS:       accepted by AAAI'20
HIGHLIGHT:      Different from existing methods, in this paper, we propose to generate cross-modality paired-images and perform both global set-level and fine-grained instance-level alignments.

19, TITLE:      Relationship-Embedded Representation Learning for Grounding Referring Expressions
http://arxiv.org/abs/1906.04464
AUTHORS:        Sibei Yang ; Guanbin Li ; Yizhou Yu
COMMENTS:       This paper is going to appear in TPAMI
HIGHLIGHT:      In this paper, we propose a Cross-Modal Relationship Extractor (CMRE) to adaptively highlight objects and relationships (spatial and semantic relations) related to the given expression with a cross-modal attention mechanism, and represent the extracted information as a language-guided visual relation graph.

20, TITLE:      Visual Odometry Revisited: What Should Be Learnt?
http://arxiv.org/abs/1909.09803
AUTHORS:        Huangying Zhan ; Chamara Saroj Weerasekera ; Jiawang Bian ; Ian Reid
COMMENTS:       ICRA2020. Demo video: https://youtu.be/Nl8mFU4SJKY Code: https://github.com/Huangying-Zhan/DF-VO
HIGHLIGHT:      In this work we present a monocular visual odometry (VO) algorithm which leverages geometry-based methods and deep learning.

21, TITLE:      Generating Interpretable Poverty Maps using Object Detection in Satellite Images
http://arxiv.org/abs/2002.01612
AUTHORS:        Kumar Ayush ; Burak Uzkent ; Marshall Burke ; David Lobell ; Stefano Ermon
HIGHLIGHT:      Here we demonstrate an interpretable computational framework to accurately predict poverty at a local level by applying object detectors to high resolution (30cm) satellite images.

22, TITLE:      VL-BERT: Pre-training of Generic Visual-Linguistic Representations
http://arxiv.org/abs/1908.08530
AUTHORS:        Weijie Su ; Xizhou Zhu ; Yue Cao ; Bin Li ; Lewei Lu ; Furu Wei ; Jifeng Dai
COMMENTS:       Accepted by ICLR 2020
HIGHLIGHT:      We introduce a new pre-trainable generic representation for visual-linguistic tasks, called Visual-Linguistic BERT (VL-BERT for short).

23, TITLE:      Spatiotemporal Tile-based Attention-guided LSTMs for Traffic Video Prediction
http://arxiv.org/abs/1910.11030
AUTHORS:        Tu Nguyen
COMMENTS:       Neurips 2019 Traffic4Cast Challenge
HIGHLIGHT:      This extended abstract describes our solution for the Traffic4Cast Challenge 2019.

24, TITLE:      RPGAN: GANs Interpretability via Random Routing
http://arxiv.org/abs/1912.10920
AUTHORS:        Andrey Voynov ; Artem Babenko
HIGHLIGHT:      In this paper, we introduce Random Path Generative Adversarial Network (RPGAN) -- an alternative design of GANs that can serve as a tool for generative model analysis.

25, TITLE:      LBP-HOG Descriptor Based on Matrix Projection For Mammogram Classification
http://arxiv.org/abs/1904.00187
AUTHORS:        Zainab Alhakeem ; Se-In Jang
COMMENTS:       5 pages, conference
HIGHLIGHT:      In this paper, we propose a Matrix based Local Binary Pattern (M-LBP) and a Matrix based Histogram of Oriented Gradients (M-HOG) descriptors based on global matrix projection.

26, TITLE:      Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis
http://arxiv.org/abs/1909.01520
AUTHORS:        Tyler L. Hayes ; Christopher Kanan
HIGHLIGHT:      Here, we revisit streaming linear discriminant analysis, which has been widely used in the data mining research community.

27, TITLE:      Probabilistic Condition Number Estimates for Real Polynomial Systems II: Structure and Smoothed Analysis
http://arxiv.org/abs/1809.03626
AUTHORS:        Alperen A. Ergür ; Grigoris Paouris ; J. Maurice Rojas
COMMENTS:       Revision to improve readability incorporating some referee comments. The introduction is revised, and an appendix is added. The mathematical content of the paper remains unchanged
HIGHLIGHT:      We consider the sensitivity of real zeros of structured polynomial systems to perturbations of their coefficients.

28, TITLE:      Interpretation and Simplification of Deep Forest
http://arxiv.org/abs/2001.04721
AUTHORS:        Sangwon Kim ; Mira Jeong ; Byoung Chul Ko
COMMENTS:       There are fatal flaws in the algorithm and we want to withdraw it
HIGHLIGHT:      This paper proposes a new method for interpreting and simplifying a black box model of a deep random forest (RF) using a proposed rule elimination.

29, TITLE:      Few-shot Text Classification with Distributional Signatures
http://arxiv.org/abs/1908.06039
AUTHORS:        Yujia Bao ; Menghua Wu ; Shiyu Chang ; Regina Barzilay
COMMENTS:       ICLR 2020
HIGHLIGHT:      In this paper, we explore meta-learning for few-shot text classification.

30, TITLE:      Classification of distributed binary labeling problems
http://arxiv.org/abs/1911.13294
AUTHORS:        Alkida Balliu ; Sebastian Brandt ; Yuval Efron ; Juho Hirvonen ; Yannic Maus ; Dennis Olivetti ; Jukka Suomela
HIGHLIGHT:      We present a complete classification of the deterministic distributed time complexity for a family of graph problems: binary labeling problems in trees.

31, TITLE:      How much does randomness help with locally checkable problems?
http://arxiv.org/abs/1902.06803
AUTHORS:        Alkida Balliu ; Sebastian Brandt ; Dennis Olivetti ; Jukka Suomela
HIGHLIGHT:      We show that such problems exist: for example, there is an LCL with deterministic complexity $\Theta(\log^2 n)$ rounds and randomized complexity $\Theta(\log n \log \log n)$ rounds.

32, TITLE:      Class-dependent Compression of Deep Neural Networks
http://arxiv.org/abs/1909.10364
AUTHORS:        Rahim Entezari ; Olga Saukh
HIGHLIGHT:      Motivated by the success of the lottery ticket hypothesis, in this paper we propose an iterative deep model compression technique, which keeps the number of false negatives of the compressed model close to the one of the original model at the price of increasing the number of false positives if necessary.

33, TITLE:      BigEarthNet Dataset with A New Class-Nomenclature for Remote Sensing Image Understanding
http://arxiv.org/abs/2001.06372
AUTHORS:        Gencer Sumbul ; Jian Kang ; Tristan Kreuziger ; Filipe Marcelino ; Hugo Costa ; Pedro Benevides ; Mario Caetano ; Begüm Demir
COMMENTS:       Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence
HIGHLIGHT:      This paper presents BigEarthNet that is a large-scale Sentinel-2 multispectral image dataset with a new class nomenclature to advance deep learning (DL) studies in remote sensing (RS).

34, TITLE:      Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
http://arxiv.org/abs/2001.08735
AUTHORS:        Hung-Yu Tseng ; Hsin-Ying Lee ; Jia-Bin Huang ; Ming-Hsuan Yang
COMMENTS:       ICLR 2020 (Spotlight). Project page: http://vllab.ucmerced.edu/ym41608/projects/CrossDomainFewShot Code: https://github.com/hytseng0509/CrossDomainFewShot
HIGHLIGHT:      In this work, we address the problem of few-shot classification under domain shifts for metric-based methods.

35, TITLE:      Behavioural Repertoire via Generative Adversarial Policy Networks
http://arxiv.org/abs/1811.02945
AUTHORS:        Marija Jegorova ; Stéphane Doncieux ; Timothy Hospedales
COMMENTS:       In Proceedings of 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pages 320 - 326
HIGHLIGHT:      In this paper, we explore a novel realization of this vision by learning a generative model over policies.

36, TITLE:      Group Fairness in Bandit Arm Selection
http://arxiv.org/abs/1912.03802
AUTHORS:        Candice Schumann ; Zhi Lang ; Nicholas Mattei ; John P. Dickerson
HIGHLIGHT:      In this work we explore two definitions of fairness: equal group probability, wherein the probability of pulling an arm from any of the protected groups is the same; and proportional parity, wherein the probability of choosing an arm from a particular group is proportional to the size of that group.

37, TITLE:      Demystifying Inter-Class Disentanglement
http://arxiv.org/abs/1906.11796
AUTHORS:        Aviv Gabbay ; Yedid Hoshen
COMMENTS:       ICLR 2020. Project page: http://www.vision.huji.ac.il/lord
HIGHLIGHT:      We therefore introduce LORD, a novel method based on Latent Optimization for Representation Disentanglement.

38, TITLE:      Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
http://arxiv.org/abs/2002.03754
AUTHORS:        Andrey Voynov ; Artem Babenko
HIGHLIGHT:      In this paper, we introduce an unsupervised method to identify interpretable directions in the latent space of a pretrained GAN model.

39, TITLE:      Quadratic Suffices for Over-parametrization via Matrix Chernoff Bound
http://arxiv.org/abs/1906.03593
AUTHORS:        Zhao Song ; Xin Yang
HIGHLIGHT:      We improve the over-parametrization size over two beautiful results [Li and Liang' 2018] and [Du, Zhai, Poczos and Singh' 2019] in deep learning theory.

40, TITLE:      A Single RGB Camera Based Gait Analysis with a Mobile Tele-Robot for Healthcare
http://arxiv.org/abs/2002.04700
AUTHORS:        Ziyang Wang
HIGHLIGHT:      The purpose of this work is twofold, the software focuses on the analysis of gait, which is widely adopted for joint correction and assessing any lower limb or spinal problem.

41, TITLE:      Deep Reinforcement-Learning-based Driving Policy for Autonomous Road Vehicles
http://arxiv.org/abs/1907.05246
AUTHORS:        Konstantinos Makantasis ; Maria Kontorinaki ; Ioannis Nikolos
COMMENTS:       19 pages. arXiv admin note: substantial text overlap with arXiv:1905.09046
HIGHLIGHT:      In this work the problem of path planning for an autonomous vehicle that moves on a freeway is considered.

42, TITLE:      Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering
http://arxiv.org/abs/1802.04397
AUTHORS:        Bryon Aragam ; Chen Dan ; Eric P. Xing ; Pradeep Ravikumar
COMMENTS:       35 pages, to appear in the Annals of Statistics
HIGHLIGHT:      Motivated by problems in data clustering, we establish general conditions under which families of nonparametric mixture models are identifiable, by introducing a novel framework involving clustering overfitted \emph{parametric} (i.e. misspecified) mixture models.

43, TITLE:      Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
http://arxiv.org/abs/1905.12019
AUTHORS:        Martin Mundt ; Sagnik Majumder ; Iuliia Pliushch ; Visvanathan Ramesh
HIGHLIGHT:      We introduce a probabilistic approach to unify deep continual learning with open set recognition, based on variational Bayesian inference.

44, TITLE:      Reformer: The Efficient Transformer
http://arxiv.org/abs/2001.04451
AUTHORS:        Nikita Kitaev ; Łukasz Kaiser ; Anselm Levskaya
COMMENTS:       ICLR 2020
HIGHLIGHT:      We introduce two techniques to improve the efficiency of Transformers.

45, TITLE:      Hybrid Compositional Reasoning for Reactive Synthesis from Finite-Horizon Specifications
http://arxiv.org/abs/1911.08145
AUTHORS:        Suguman Bansal ; Yong Li ; Lucas M. Tabajara ; Moshe Y. Vardi
COMMENTS:       Accepted by AAAI 2020. Tool Lisa for (a). LTLf to DFA conversion, and (b). LTLf synthesis can be found here: https://github.com/vardigroup/lisa
HIGHLIGHT:      This work proposes a hybrid representation approach for the conversion.

46, TITLE:      R-MADDPG for Partially Observable Environments and Limited Communication
http://arxiv.org/abs/2002.06684
AUTHORS:        Rose E. Wang ; Michael Everett ; Jonathan P. How
COMMENTS:       Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 36th International Conference on Machine Learning, Long Beach, California, USA, 2019
HIGHLIGHT:      This paper introduces a deep recurrent multiagent actor-critic framework (R-MADDPG) for handling multiagent coordination under partial observable set-tings and limited communication.