2020.06.22.txt

==========New Papers==========
1, TITLE:       Concatenated Attention Neural Network for Image Restoration
http://arxiv.org/abs/2006.11162
AUTHORS:        Tian YingJie ; Wang YiQi ; Yang LinRui ; Qi ZhiQuan
HIGHLIGHT:      In this paper, we present a general framework for low-level vision tasks including image compression artifacts reduction and image denoising.

2, TITLE:       Emotion Recognition on large video dataset based on Convolutional Feature Extractor and Recurrent Neural Network
http://arxiv.org/abs/2006.11168
AUTHORS:        Denis Rangulov ; Muhammad Fahim
COMMENTS:       6 pages, 7 figures, Face and Gesture 2020 Workshop Paper (ABAW2020 competition)
HIGHLIGHT:      In this study, we consider the emotion recognition task as a classification as well as a regression task by processing encoded emotions in different datasets using deep learning models.

3, TITLE:       Evaluation Of Hidden Markov Models Using Deep CNN Features In Isolated Sign Recognition
http://arxiv.org/abs/2006.11183
AUTHORS:        Anil Osman Tur ; Hacer Yalim Keles
COMMENTS:       This paper is under review at Multimedia Tools and Applications Journal. It contains 16 pages, 5 figure, 8 tables
HIGHLIGHT:      In this study, we provide a framework that is composed of three modules to solve isolated sign recognition problem using different sequence models.

4, TITLE:       Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
http://arxiv.org/abs/2006.11184
AUTHORS:        Jeff Calder ; Brendan Cook ; Matthew Thorpe ; Dejan Slepcev
COMMENTS:       To appear in the Proceedings of the 37th International Conference on Machine Learning (ICML 2020)
HIGHLIGHT:      We propose a new framework, called Poisson learning, for graph based semi-supervised learning at very low label rates.

5, TITLE:       Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks
http://arxiv.org/abs/2006.11193
AUTHORS:        Sergio Pereira ; Adriano Pinto ; Joana Amorim ; Alexandrine Ribeiro ; Victor Alves ; Carlos A. Silva
COMMENTS:       Published in IEEE Transactions on Medical Imaging (TMI)
HIGHLIGHT:      In this paper, we propose recombination of features and a spatially adaptive recalibration block that is adapted for semantic segmentation with Fully Convolutional Networks - the SegSE block.

6, TITLE:       Bootstrapping Complete The Look at Pinterest
http://arxiv.org/abs/2006.10792
AUTHORS:        Eileen Li ; Eric Kim ; Andrew Zhai ; Josh Beal ; Kunlong Gu
COMMENTS:       9 pages, 12 figures, To be published in KDD '20
HIGHLIGHT:      In this paper, we will describe how we bootstrapped the Complete The Look (CTL) system at Pinterest. We will introduce our outfit dataset of over 1 million outfits and 4 million objects, a subset of which we will make available to the research community, and describe the pipeline used to obtain and refresh this dataset.

7, TITLE:       Learn to Earn: Enabling Coordination within a Ride Hailing Fleet
http://arxiv.org/abs/2006.10904
AUTHORS:        Harshal A. Chaudhari ; John W. Byers ; Evimaria Terzi
COMMENTS:       16 pages, 9 figures
HIGHLIGHT:      An ideal solution aims to minimize the response time for each hyper local passenger ride request, while simultaneously maintaining high demand satisfaction and supply utilization across the entire city.

8, TITLE:       Neural Topic Modeling with Continual Lifelong Learning
http://arxiv.org/abs/2006.10909
AUTHORS:        Pankaj Gupta ; Yatin Chaudhary ; Thomas Runkler ; Hinrich Schütze
COMMENTS:       ICML2020
HIGHLIGHT:      To address the problem, we propose a lifelong learning framework for neural topic modeling that can continuously process streams of document collections, accumulate topics and guide future topic modeling tasks by knowledge transfer from several sources to better deal with the sparse data.

9, TITLE:       Probabilistic Fair Clustering
http://arxiv.org/abs/2006.10916
AUTHORS:        Seyed A. Esmaeili ; Brian Brubach ; Leonidas Tsepenekas ; John P. Dickerson
HIGHLIGHT:      We present clustering algorithms in this more general setting with approximation ratio guarantees.

10, TITLE:      DS6: Deformation-aware learning for small vessel segmentation with small, imperfectly labeled dataset
http://arxiv.org/abs/2006.10802
AUTHORS:        Soumick Chatterjee ; Kartik Prabhu ; Mahantesh Pattadkal ; Gerda Bortsova ; Florian Dubost ; Hendrik Mattern ; Marleen de Bruijne ; Oliver Speck ; Andreas Nürnberger
HIGHLIGHT:      In this paper, we put forth a study that incorporates deep learning techniques to automatically segment these LSA using 3D 7 Tesla Time-of-fight Magnetic Resonance Angiogram images.

11, TITLE:      Frustratingly Simple Domain Generalization via Image Stylization
http://arxiv.org/abs/2006.11207
AUTHORS:        Nathan Somavarapu ; Chih-Yao Ma ; Zsolt Kira
COMMENTS:       Code: https://github.com/GT-RIPL/DomainGeneralization-Stylization
HIGHLIGHT:      In this work, we address the Domain Generalization problem, where the classifier must generalize to an unknown target domain.

12, TITLE:      Hyperparameter Analysis for Image Captioning
http://arxiv.org/abs/2006.10923
AUTHORS:        Amish Patel ; Aravind Varier
COMMENTS:       10 pages, 9 figures, and 7 tables
HIGHLIGHT:      In this paper, we perform a thorough sensitivity analysis on state-of-the-art image captioning approaches using two different architectures: CNN+LSTM and CNN+Transformer.

13, TITLE:      Recovering Petaflops in Contrastive Semi-Supervised Learning of Visual Representations
http://arxiv.org/abs/2006.10803
AUTHORS:        Mahmoud Assran ; Nicolas Ballas ; Lluis Castrejon ; Michael Rabbat
HIGHLIGHT:      We propose a semi-supervised loss, SuNCEt, based on noise-contrastive estimation, that aims to distinguish examples of different classes in addition to the self-supervised instance-wise pretext tasks.

14, TITLE:      Semantic Linking Maps for Active Visual Object Search
http://arxiv.org/abs/2006.10807
AUTHORS:        Zhen Zeng ; Adrian Röfer ; Odest Chadwicke Jenkins
COMMENTS:       Published in ICRA 2020 (Best Paper Award in Cognitive Robotics)
HIGHLIGHT:      In this paper, we propose an active visual object search strategy method through our introduction of the Semantic Linking Maps (SLiM) model.

15, TITLE:      An adversarial algorithm for variational inference with a new role for acetylcholine
http://arxiv.org/abs/2006.10811
AUTHORS:        Ari S. Benjamin ; Konrad P. Kording
HIGHLIGHT:      We implement this algorithm and show that it can successfully train the approximate inference network for generative models.

16, TITLE:      Unified Representation Learning for Efficient Medical Image Analysis
http://arxiv.org/abs/2006.11223
AUTHORS:        Ghada Zamzmi ; Sivaramakrishnan Rajaraman ; Sameer Antani
HIGHLIGHT:      We propose a novel multitask deep learning-based approach, called unified representation (U-Rep), that can be used to simultaneously perform several medical image analysis tasks.

17, TITLE:      Lookahead Adversarial Semantic Segmentation
http://arxiv.org/abs/2006.11227
AUTHORS:        Hadi Jamali-Rad ; Attila Szabo ; Matteo Presutto
COMMENTS:       22 pages
HIGHLIGHT:      To battle the stability issues, we introduce a novel lookahead adversarial learning approach (LoAd) with an embedded label map aggregation module.

18, TITLE:      Cooperative Multi-Agent Reinforcement Learning with Partial Observations
http://arxiv.org/abs/2006.10822
AUTHORS:        Yan Zhang ; Michael M. Zavlanos
HIGHLIGHT:      In this paper, we propose a distributed zeroth-order policy optimization method for Multi-Agent Reinforcement Learning (MARL).

19, TITLE:      Joint Speaker Counting, Speech Recognition, and Speaker Identification for Overlapped Speech of Any Number of Speakers
http://arxiv.org/abs/2006.10930
AUTHORS:        Naoyuki Kanda ; Yashesh Gaur ; Xiaofei Wang ; Zhong Meng ; Zhuo Chen ; Tianyan Zhou ; Takuya Yoshioka
COMMENTS:       Submitted to INTERSPEECH 2020
HIGHLIGHT:      In this paper, we propose a joint model for simultaneous speaker counting, speech recognition, and speaker identification on monaural overlapped speech.

20, TITLE:      Particle Swarm Optimization with Velocity Restriction and Evolutionary Parameters Selection for Scheduling Problem
http://arxiv.org/abs/2006.10935
AUTHORS:        Pavel Matrenin ; Viktor Sekaev
HIGHLIGHT:      The article presents a study of the Particle Swarm optimization method for scheduling problem.

21, TITLE:      The cyclic job-shop scheduling problem: The new subclass of the job-shop problem and applying the Simulated annealing to solve it
http://arxiv.org/abs/2006.10938
AUTHORS:        Pavel Matrenin ; Vadim Manusov
HIGHLIGHT:      In the paper, the new approach to the scheduling problem are described.

22, TITLE:      Consistency Guided Scene Flow Estimation
http://arxiv.org/abs/2006.11242
AUTHORS:        Yuhua Chen ; Luc Van Gool ; Cordelia Schmid ; Cristian Sminchisescu
HIGHLIGHT:      We present Consistency Guided Scene Flow Estimation (CGSF), a framework for joint estimation of 3D scene structure and motion from stereo videos.

23, TITLE:      Deep Transformation-Invariant Clustering
http://arxiv.org/abs/2006.11132
AUTHORS:        Tom Monnier ; Thibault Groueix ; Mathieu Aubry
COMMENTS:       Project webpage: http://imagine.enpc.fr/~monniert/DTIClustering/
HIGHLIGHT:      In contrast, we present an orthogonal approach that does not rely on abstract features but instead learns to predict image transformations and performs clustering directly in image space.

24, TITLE:      Melanoma Diagnosis with Spatio-Temporal Feature Learning on Sequential Dermoscopic Images
http://arxiv.org/abs/2006.10950
AUTHORS:        Zhen Yu ; Jennifer Nguyen ; Xiaojun Chang ; John Kelly ; Catriona Mclean ; Lei Zhang ; Victoria Mar ; Zongyuan Ge
COMMENTS:       submission of miccai 2020
HIGHLIGHT:      Based on the fact that dermatologists diagnose ambiguous skin lesions by evaluating the dermoscopic changes over time via follow-up examination, in this study, we propose an automated framework for melanoma diagnosis using sequential dermoscopic images.

25, TITLE:      Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy
http://arxiv.org/abs/2006.11135
AUTHORS:        Quentin Renau ; Carola Doerr ; Johann Dreo ; Benjamin Doerr
COMMENTS:       To appear in the proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN 2020)
HIGHLIGHT:      In this work, we analyze how the sampling method and the sample size influence the quality of the feature value approximations and how this quality impacts the accuracy of a standard classification task.

26, TITLE:      When Is Amplification Necessary for Composition in Randomized Query Complexity?
http://arxiv.org/abs/2006.10957
AUTHORS:        Shalev Ben-David ; Mika Göös ; Robin Kothari ; Thomas Watson
COMMENTS:       17 pages. Accepted to RANDOM 2020
HIGHLIGHT:      We study the question: When is this log factor necessary?

27, TITLE:      Keep Your AI-es on the Road: Tackling Distracted Driver Detection with Convolutional Neural Networks and Targetted Data Augmentation
http://arxiv.org/abs/2006.10955
AUTHORS:        Nikka Mofid ; Jasmine Bayrooti ; Shreya Ravi
COMMENTS:       10 pages, 11 figures
HIGHLIGHT:      In our study, we tackle the problem of distracted driving by aiming to build a robust multi-class classifier to detect and identify different forms of driver inattention using the State Farm Distracted Driving Dataset.

28, TITLE:      New Vietnamese Corpus for Machine ReadingComprehension of Health News Articles
http://arxiv.org/abs/2006.11138
AUTHORS:        Kiet Van Nguyen ; Duc-Vu Nguyen ; Anh Gia-Tuan Nguyen ; Ngan Luu-Thuy Nguyen
HIGHLIGHT:      In this study, we present ViNewsQA as a new corpus for the low-resource Vietnamese language to evaluate models of machine reading comprehension.

29, TITLE:      A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19
http://arxiv.org/abs/2006.10964
AUTHORS:        David Oniani ; Yanshan Wang
HIGHLIGHT:      In this paper, we propose to apply a language model for automatically answering questions related to COVID-19 and qualitatively evaluate the generated responses.

30, TITLE:      Attention Mesh: High-fidelity Face Mesh Prediction in Real-time
http://arxiv.org/abs/2006.10962
AUTHORS:        Ivan Grishchenko ; Artsiom Ablavatski ; Yury Kartynnik ; Karthik Raveendran ; Matthias Grundmann
COMMENTS:       4 pages, 5 figures; CVPR Workshop on Computer Vision for Augmented and Virtual Reality, Seattle, WA, USA, 2020
HIGHLIGHT:      We present Attention Mesh, a lightweight architecture for 3D face mesh prediction that uses attention to semantically meaningful regions.

31, TITLE:      Pupil Center Detection Approaches: A comparative analysis
http://arxiv.org/abs/2006.11147
AUTHORS:        Talía Vázquez Romaguera ; Liset Vázquez Romaguera ; David Castro Piñol ; Carlos Román Vázquez Seisdedos
COMMENTS:       15 pages, 9 figures, submitted to the journal "Computaci\'on y Sistemas"
HIGHLIGHT:      In this work, we aim at comparing four of the most frequently cited traditional methods for pupil center detection in terms of accuracy, robustness, and computational cost.

32, TITLE:      Compositional Learning of Image-Text Query for Image Retrieval
http://arxiv.org/abs/2006.11149
AUTHORS:        Muhammad Umer Anwaar ; Egor Labintcev ; Martin Kleinsteuber
HIGHLIGHT:      In this paper, we investigate the problem of retrieving images from a database based on a multi-modal (image-text) query.

33, TITLE:      Sentiment Frames for Attitude Extraction in Russian
http://arxiv.org/abs/2006.10973
AUTHORS:        Natalia Loukachevitch ; Nicolay Rusnachenko
COMMENTS:       12 pages, 1 figure, 6 tables
HIGHLIGHT:      In this paper, we described the lexicon RuSentiFrames for Russian, where predicate words and expressions are collected and linked to so-called sentiment frames conveying several types of presupposed information on attitudes and effects.

34, TITLE:      Common equivalence and size after forgetting
http://arxiv.org/abs/2006.11152
AUTHORS:        Paolo Liberatore
HIGHLIGHT:      An algorithm for forgetting and checking common equivalence in polynomial space is given for the Horn case; it is polynomial-time for the subclass of single-head formulae.

35, TITLE:      Full complexity classification of the list homomorphism problem for bounded-treewidth graphs
http://arxiv.org/abs/2006.11155
AUTHORS:        Karolina Okrasa ; Marta Piecyk ; Paweł Rzążewski
COMMENTS:       The extended abstract of the paper was accepted to ESA 2020
HIGHLIGHT:      In this paper we extend and generalize their results for \emph{all} relevant graphs $H$, i.e., those, for which the LHom{H} problem is NP-hard.

36, TITLE:      Representing Pure Nash Equilibria in Argumentation
http://arxiv.org/abs/2006.11020
AUTHORS:        Bruno Yun ; Srdjan Vesic ; Nir Oren
HIGHLIGHT:      In this paper we describe an argumentation-based representation of normal form games, and demonstrate how argumentation can be used to compute pure strategy Nash equilibria.

37, TITLE:      Graphs with Multiple Sources per Vertex
http://arxiv.org/abs/2006.11159
AUTHORS:        Martin van Harmelen ; Jonas Groschwitz
COMMENTS:       Supervision by Jonas Groschwitz
HIGHLIGHT:      Graphs with Multiple Sources per Vertex

38, TITLE:      Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm
http://arxiv.org/abs/2006.11026
AUTHORS:        Arina Buzdalova ; Carola Doerr ; Anna Rodionova
COMMENTS:       To appear in the Proceedings of Parallel Problem Solving from Nature (PPSN'2020)
HIGHLIGHT:      We introduce in this work a new hybrid parameter control technique, which combines the well-known one-fifth success rule with Q-learning.

39, TITLE:      An operator view of policy gradient methods
http://arxiv.org/abs/2006.11266
AUTHORS:        Dibya Ghosh ; Marlos C. Machado ; Nicolas Le Roux
HIGHLIGHT:      We cast policy gradient methods as the repeated application of two operators: a policy improvement operator $\mathcal{I}$, which maps any policy $\pi$ to a better one $\mathcal{I}\pi$, and a projection operator $\mathcal{P}$, which finds the best approximation of $\mathcal{I}\pi$ in the set of realizable policies.

40, TITLE:      NROWAN-DQN: A Stable Noisy Network with Noise Reduction and Online Weight Adjustment for Exploration
http://arxiv.org/abs/2006.10980
AUTHORS:        Shuai Han ; Wenbo Zhou ; Jing Liu ; Shuai Lü
HIGHLIGHT:      Based on NoisyNets, this paper proposes an algorithm called NROWAN-DQN, i.e., Noise Reduction and Online Weight Adjustment NoisyNet-DQN.

41, TITLE:      Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks
http://arxiv.org/abs/2006.11029
AUTHORS:        Andreas Venzke ; Guannan Qu ; Steven Low ; Spyros Chatzivasileiadis
COMMENTS:       The code to reproduce the simulation results is available https://doi.org/10.5281/zenodo.3871755
HIGHLIGHT:      This paper introduces for the first time a framework to obtain provable worst-case guarantees for neural network performance, using learning for optimal power flow (OPF) problems as a guiding example.

42, TITLE:      On Reward-Free Reinforcement Learning with Linear Function Approximation
http://arxiv.org/abs/2006.11274
AUTHORS:        Ruosong Wang ; Simon S. Du ; Lin F. Yang ; Ruslan Salakhutdinov
HIGHLIGHT:      In this work, we give both positive and negative results for reward-free RL with linear function approximation.

43, TITLE:      Center-based 3D Object Detection and Tracking
http://arxiv.org/abs/2006.11275
AUTHORS:        Tianwei Yin ; Xingyi Zhou ; Philipp Krähenbühl
HIGHLIGHT:      In this paper, we instead propose to represent, detect, and track 3D objects as points.

44, TITLE:      Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift
http://arxiv.org/abs/2006.10990
AUTHORS:        Qinming Zhang ; Luyan Liu ; Kai Ma ; Cheng Zhuo ; Yefeng Zheng
COMMENTS:       Accepted by IJCAI 2020
HIGHLIGHT:      In this paper, we propose a novel robust cross-denoising framework using two peer networks to address domain shift and corrupted label problems with a peer-review strategy.

45, TITLE:      Wave Propagation of Visual Stimuli in Focus of Attention
http://arxiv.org/abs/2006.11035
AUTHORS:        Lapo Faggi ; Alessandro Betti ; Dario Zanca ; Stefano Melacci ; Marco Gori
HIGHLIGHT:      In this paper, we present a biologically-plausible computational model of focus of attention that exhibits spatiotemporal locality and that is very well-suited for parallel and distributed implementations.

46, TITLE:      Deep Image Translation for Enhancing Simulated Ultrasound Images
http://arxiv.org/abs/2006.10850
AUTHORS:        Lin Zhang ; Tiziano Portenier ; Christoph Paulus ; Orcun Goksel
HIGHLIGHT:      In this work we introduce a deep learning approach based on adversarial training that mitigates this trade-off by improving the quality of simulated images with constant computation time.

47, TITLE:      Image classification in frequency domain with 2SReLU: a second harmonics superposition activation function
http://arxiv.org/abs/2006.10853
AUTHORS:        Thomio Watanabe ; Denis F. Wolf
COMMENTS:       12 pages, 9 figures
HIGHLIGHT:      In this work, an image classification Convolutional Neural Network and its building blocks are described from a frequency domain perspective.

48, TITLE:      Shop The Look: Building a Large Scale Visual Shopping System at Pinterest
http://arxiv.org/abs/2006.10866
AUTHORS:        Raymond Shiau ; Hao-Yu Wu ; Eric Kim ; Yue Li Du ; Anqi Guo ; Zhiyuan Zhang ; Eileen Li ; Kunlong Gu ; Charles Rosenberg ; Andrew Zhai
COMMENTS:       10 pages, 7 figures, Accepted to KDD'20
HIGHLIGHT:      In this work, we provide a holistic view of how we built Shop The Look, a shopping oriented visual search system, along with lessons learned from addressing shopping needs.

49, TITLE:      A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures
http://arxiv.org/abs/2006.11056
AUTHORS:        Meishan Zhang
COMMENTS:       SCIENCE CHINA Technological Sciences
HIGHLIGHT:      This article aims for a brief survey on this topic.

50, TITLE:      Generative Patch Priors for Practical Compressive Image Recovery
http://arxiv.org/abs/2006.10873
AUTHORS:        Rushil Anirudh ; Suhas Lohit ; Pavan Turaga
HIGHLIGHT:      In this paper, we propose the generative patch prior (GPP) that defines a generative prior for compressive image recovery, based on patch-manifold models.

51, TITLE:      Dataset for Automatic Summarization of Russian News
http://arxiv.org/abs/2006.11063
AUTHORS:        Ilya Gusev
COMMENTS:       Submitted to AINL 2020
HIGHLIGHT:      We describe the properties of this dataset and benchmark several extractive and abstractive models.

52, TITLE:      Recommendations for Emerging Air Taxi Network Operations based on Online Review Analysis of Helicopter Services
http://arxiv.org/abs/2006.10898
AUTHORS:        Suchithra Rajendran ; Emily Pagel
HIGHLIGHT:      The insights obtained in this paper could assist any air taxi companies in providing better customer service when they venture into the market.

53, TITLE:      A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging
http://arxiv.org/abs/2006.11117
AUTHORS:        Davood Karimi ; Lana Vasung ; Camilo Jaimes ; Fedel Machado-Rivas ; Shadab Khan ; Simon K. Warfield ; Ali Gholipour
HIGHLIGHT:      In this paper, we propose a machine learning-based technique that can accurately estimate the number and orientations of fascicles in a voxel.

54, TITLE:      From Discrete to Continuous Convolution Layers
http://arxiv.org/abs/2006.11120
AUTHORS:        Assaf Shocher ; Ben Feinstein ; Niv Haim ; Michal Irani
HIGHLIGHT:      We propose a generalization of the common Conv-layer, from a discrete layer to a Continuous Convolution (CC) Layer.

55, TITLE:      An Integer Linear Programming Framework for Mining Constraints from Data
http://arxiv.org/abs/2006.10836
AUTHORS:        Tao Meng ; Kai-Wei Chang
COMMENTS:       12 pages, 2 figures, submitted to NeurIPS2020
HIGHLIGHT:      To this end, we present a general integer linear programming (ILP) framework for mining constraints from data.

56, TITLE:      Deep Learning-based Single Image Face Depth Data Enhancement
http://arxiv.org/abs/2006.11091
AUTHORS:        Torsten Schlett ; Christian Rathgeb ; Christoph Busch
HIGHLIGHT:      This work proposes a deep learning-based face depth enhancement method.

57, TITLE:      Exploring Processing of Nested Dependencies in Neural-Network Language Models and Humans
http://arxiv.org/abs/2006.11098
AUTHORS:        Yair Lakretz ; Dieuwke Hupkes ; Alessandra Vergallito ; Marco Marelli ; Marco Baroni ; Stanislas Dehaene
HIGHLIGHT:      Overall, our study shows that exploring the ways in which modern artificial neural networks process sentences leads to precise and testable hypotheses about human linguistic performance.

58, TITLE:      Oscillatory background activity implements a backbone for sampling-based computations in spiking neural networks
http://arxiv.org/abs/2006.11099
AUTHORS:        Michael G. Müller ; Robert Legenstein
COMMENTS:       18 pages, 8 figures
HIGHLIGHT:      We propose that background oscillations, an ubiquitous phenomenon throughout the brain, can mitigate this issue and thus implement the backbone for sampling-based computations in spiking neural networks.

59, TITLE:      Contextual and Possibilistic Reasoning for Coalition Formation
http://arxiv.org/abs/2006.11097
AUTHORS:        Antonis Bikakis ; Patrice Caire
HIGHLIGHT:      In this article, we address the question of how to find and evaluate coalitions among agents in multiagent systems using MCS tools, while taking into consideration the uncertainty around the agents' actions.

60, TITLE:      Learning non-rigid surface reconstruction from spatio-temporal image patches
http://arxiv.org/abs/2006.10841
AUTHORS:        Matteo Pedone ; Abdelrahman Mostafa ; Janne heikkilä
HIGHLIGHT:      We present a method to reconstruct a dense spatio-temporal depth map of a non-rigidly deformable object directly from a video sequence. Since the geometric complexity of a local spatio-temporal patch of a deforming non-rigid object is often simple enough to be faithfully represented with a parametric model, we artificially generate a database of small deforming rectangular meshes rendered with different material properties and light conditions, along with their corresponding depth videos, and use such data to train a convolutional neural network.

61, TITLE:      Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features
http://arxiv.org/abs/2006.10848
AUTHORS:        Robin Tibor Schirrmeister ; Yuxuan Zhou ; Tonio Ball ; Dan Zhang
HIGHLIGHT:      To remove the negative impact of model bias and domain prior on detecting high-level differences, we propose two methods, first, using the log likelihood ratios of two identical models, one trained on the in-distribution data (e.g., CIFAR10) and the other one on a more general distribution of images (e.g., 80 Million Tiny Images).



==========Updates to Previous Papers==========
1, TITLE:       Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning
http://arxiv.org/abs/2002.00264
AUTHORS:        Mahesh Kumar Krishna Reddy ; Mohammad Hossain ; Mrigank Rochan ; Yang Wang
COMMENTS:       Accepted to WACV 2020
HIGHLIGHT:      Given a target camera scene, our goal is to adapt a model to this specific scene with only a few labeled images of that scene.

2, TITLE:       Joey NMT: A Minimalist NMT Toolkit for Novices
http://arxiv.org/abs/1907.12484
AUTHORS:        Julia Kreutzer ; Jasmijn Bastings ; Stefan Riezler
HIGHLIGHT:      We present Joey NMT, a minimalist neural machine translation toolkit based on PyTorch that is specifically designed for novices.

3, TITLE:       Making deep neural networks right for the right scientific reasons by interacting with their explanations
http://arxiv.org/abs/2001.05371
AUTHORS:        Patrick Schramowski ; Wolfgang Stammer ; Stefano Teso ; Anna Brugger ; Xiaoting Shao ; Hans-Georg Luigs ; Anne-Katrin Mahlein ; Kristian Kersting
COMMENTS:       arXiv admin note: text overlap with arXiv:1805.08578
HIGHLIGHT:      In this work, we introduce the novel learning setting of "explanatory interactive learning" (XIL) and illustrate its benefits on a plant phenotyping research task.

4, TITLE:       Time-aware Large Kernel Convolutions
http://arxiv.org/abs/2002.03184
AUTHORS:        Vasileios Lioutas ; Yuhong Guo
COMMENTS:       Accepted by ICML 2020
HIGHLIGHT:      In this paper, we introduce Time-aware Large Kernel (TaLK) Convolutions, a novel adaptive convolution operation that learns to predict the size of a summation kernel instead of using a fixed-sized kernel matrix.

5, TITLE:       Few-shot Neural Architecture Search
http://arxiv.org/abs/2006.06863
AUTHORS:        Yiyang Zhao ; Linnan Wang ; Yuandong Tian ; Rodrigo Fonseca ; Tian Guo
HIGHLIGHT:      In this work, we propose few-shot NAS that explores the choice of using multiple super-nets: each super-net is pre-trained to be in charge of a sub-region of the search space.

6, TITLE:       Modeling Latent Sentence Structure in Neural Machine Translation
http://arxiv.org/abs/1901.06436
AUTHORS:        Jasmijn Bastings ; Wilker Aziz ; Ivan Titov ; Khalil Sima'an
COMMENTS:       Accepted as an extended abstract to ACL NMT workshop 2018
HIGHLIGHT:      In this work we investigate a more challenging setup: we incorporate sentence structure as a latent variable in a standard NMT encoder-decoder and induce it in such a way as to benefit the translation task.

7, TITLE:       Aligning Superhuman AI and Human Behavior: Chess as a Model System
http://arxiv.org/abs/2006.01855
AUTHORS:        Reid McIlroy-Young ; Siddhartha Sen ; Jon Kleinberg ; Ashton Anderson
HIGHLIGHT:      We develop and introduce Maia, a customized version of Alpha-Zero trained on human chess games, that predicts human moves at a much higher accuracy than existing engines, and can achieve maximum accuracy when predicting decisions made by players at a specific skill level in a tuneable way.

8, TITLE:       MOSQUITO-NET: A deep learning based CADx system for malaria diagnosis along with model interpretation using GradCam and class activation maps
http://arxiv.org/abs/2006.10547
AUTHORS:        Aayush Kumar ; Sanat B Singh ; Suresh Chandra Satapathy ; Minakhi Rout
COMMENTS:       arXiv admin note: text overlap with arXiv:2003.09871 by other authors
HIGHLIGHT:      In this paper, we evaluate the performance of custom made convnet Mosquito-Net, to classify the infected and uninfected cells for malaria diagnosis which could be deployed on the edge and mobile devices owing to its fewer parameters and less computation power.

9, TITLE:       TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP
http://arxiv.org/abs/1912.00982
AUTHORS:        Nils Rethmeier ; Vageesh Kumar Saxena ; Isabelle Augenstein
HIGHLIGHT:      Thus, for TX-Ray, we modify the established computer vision explainability principle of 'visualizing preferred inputs of neurons' to make it usable transfer analysis and NLP.

10, TITLE:      Interpretable Neural Predictions with Differentiable Binary Variables
http://arxiv.org/abs/1905.08160
AUTHORS:        Jasmijn Bastings ; Wilker Aziz ; Ivan Titov
HIGHLIGHT:      We propose a latent model that mixes discrete and continuous behaviour allowing at the same time for binary selections and gradient-based training without REINFORCE.

11, TITLE:      Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge
http://arxiv.org/abs/2006.06609
AUTHORS:        Alon Talmor ; Oyvind Tafjord ; Peter Clark ; Yoav Goldberg ; Jonathan Berant
HIGHLIGHT:      In this work, we provide a first demonstration that LMs can be trained to reliably perform systematic reasoning combining both implicit, pre-trained knowledge and explicit natural language statements.

12, TITLE:      Improving the Gating Mechanism of Recurrent Neural Networks
http://arxiv.org/abs/1910.09890
AUTHORS:        Albert Gu ; Caglar Gulcehre ; Tom Le Paine ; Matt Hoffman ; Razvan Pascanu
COMMENTS:       International Conference on Machine Learning 2020
HIGHLIGHT:      We address this problem by deriving two synergistic modifications to the standard gating mechanism that are easy to implement, introduce no additional hyperparameters, and improve learnability of the gates when they are close to saturation.

13, TITLE:      ChestX-Det10: Chest X-ray Dataset on Detection of Thoracic Abnormalities
http://arxiv.org/abs/2006.10550
AUTHORS:        Jingyu Liu ; Jie Lian ; Yizhou Yu
HIGHLIGHT:      Most existing works on chest X-rays focus on disease classification and weakly supervised localization. We provide a new benchmark called ChestX-det10, including box-level annotations of 10 categories of disease/abnormality of $\sim$ 3,500 images.

14, TITLE:      Self-training with Noisy Student improves ImageNet classification
http://arxiv.org/abs/1911.04252
AUTHORS:        Qizhe Xie ; Minh-Thang Luong ; Eduard Hovy ; Quoc V. Le
COMMENTS:       CVPR 2020
HIGHLIGHT:      We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant.

15, TITLE:      Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm
http://arxiv.org/abs/2004.07657
AUTHORS:        Muhammad Zaigham Zaheer ; Jin-ha Lee ; Marcella Astrid ; Seung-Ik Lee
COMMENTS:       Accepted at the Conference on Computer Vision and Pattern Recognition CVPR 2020. http://openaccess.thecvf.com/content_CVPR_2020/html/Zaheer_Old_Is_Gold_Redefining_the_Adversarially_Learned_One-Class_Classifier_Training_CVPR_2020_paper.html
HIGHLIGHT:      In this study, we propose a framework that effectively generates stable results across a wide range of training steps and allows us to use both the generator and the discriminator of an adversarial model for efficient and robust anomaly detection.

16, TITLE:      Estimate of the Neural Net Dimension Using Algebraic Topology and Lie Theory
http://arxiv.org/abs/2004.02881
AUTHORS:        Luciano Melodia ; Richard Lenz
COMMENTS:       The title of this article was formerly "Parameterization of Neural Networks with Connected Abelian Lie Groups as Data Manifold"
HIGHLIGHT:      In this paper we present an approach to determine the smallest possible number of perceptrons in a neural net in such a way that the topology of the input space can be learned sufficiently well.

17, TITLE:      A Multi-Objective Deep Reinforcement Learning Framework
http://arxiv.org/abs/1803.02965
AUTHORS:        Thanh Thi Nguyen ; Ngoc Duy Nguyen ; Peter Vamplew ; Saeid Nahavandi ; Richard Dazeley ; Chee Peng Lim
COMMENTS:       21 pages
HIGHLIGHT:      This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks.

18, TITLE:      Fake Generated Painting Detection via Frequency Analysis
http://arxiv.org/abs/2003.02467
AUTHORS:        Yong Bai ; Yuanfang Guo ; Jinjie Wei ; Lin Lu ; Rui Wang ; Yunhong Wang
COMMENTS:       5 pages, 6 figures, accepted by ICIP 2020
HIGHLIGHT:      Based on our observations, we propose Fake Generated Painting Detection via Frequency Analysis (FGPD-FA) by extracting three types of features in frequency domain. Besides, we also propose a digital fake painting detection database for assessing the proposed method.

19, TITLE:      Span Selection Pre-training for Question Answering
http://arxiv.org/abs/1909.04120
AUTHORS:        Michael Glass ; Alfio Gliozzo ; Rishav Chakravarti ; Anthony Ferritto ; Lin Pan ; G P Shrivatsa Bhargav ; Dinesh Garg ; Avirup Sil
COMMENTS:       Accepted at ACL2020
HIGHLIGHT:      In this paper we introduce a new pre-training task inspired by reading comprehension to better align the pre-training from memorization to understanding.

20, TITLE:      Learning Temporal Attention in Dynamic Graphs with Bilinear Interactions
http://arxiv.org/abs/1909.10367
AUTHORS:        Boris Knyazev ; Carolyn Augusta ; Graham W. Taylor
COMMENTS:       15 pages, source code is available at https://github.com/uoguelph-mlrg/LDG
HIGHLIGHT:      To alleviate these issues, we propose a model based on temporal point processes and variational autoencoders that learns to infer temporal attention between nodes by observing node communication.

21, TITLE:      Combinatory Chemistry: Towards a Simple Model of Emergent Evolution
http://arxiv.org/abs/2003.07916
AUTHORS:        Germán Kruszewski ; Tomas Mikolov
HIGHLIGHT:      To tackle this challenge, here we introduce Combinatory Chemistry, an Algorithmic Artificial Chemistry based on a minimalistic computational paradigm named Combinatory Logic.

22, TITLE:      LEAF: Latent Exploration Along the Frontier
http://arxiv.org/abs/2005.10934
AUTHORS:        Homanga Bharadhwaj ; Animesh Garg ; Florian Shkurti
COMMENTS:       Preprint. Preliminary report
HIGHLIGHT:      In this paper, we propose an exploration framework, which learns a dynamics-aware manifold of reachable states.

23, TITLE:      Panoptic Image Annotation with a Collaborative Assistant
http://arxiv.org/abs/1906.06798
AUTHORS:        Jasper R. R. Uijlings ; Mykhaylo Andriluka ; Vittorio Ferrari
HIGHLIGHT:      This paper aims to reduce the time to annotate images for panoptic segmentation, which requires annotating segmentation masks and class labels for all object instances and stuff regions.

24, TITLE:      Exploiting the Matching Information in the Support Set for Few Shot Event Classification
http://arxiv.org/abs/2002.05295
AUTHORS:        Viet Dac Lai ; Franck Dernoncourt ; Thien Huu Nguyen
COMMENTS:       Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2020
HIGHLIGHT:      We propose a novel training method for this problem that exten-sively exploit the support set during the training process of a few-shotlearning model.

25, TITLE:      Universal Lower-Bounds on Classification Error under Adversarial Attacks and Random Corruption
http://arxiv.org/abs/2006.09989
AUTHORS:        Elvis Dohmatob
HIGHLIGHT:      Our contributions are three-fold.

26, TITLE:      Jump to better conclusions: SCAN both left and right
http://arxiv.org/abs/1809.04640
AUTHORS:        Jasmijn Bastings ; Marco Baroni ; Jason Weston ; Kyunghyun Cho ; Douwe Kiela
HIGHLIGHT:      To mitigate this we propose a complementary dataset, which requires mapping actions back to the original commands, called NACS.

27, TITLE:      A generalizable saliency map-based interpretation of model outcome
http://arxiv.org/abs/2006.09504
AUTHORS:        Shailja Thakur ; Sebastian Fischmeister
HIGHLIGHT:      To fully exploit the capabilities of complex neural networks, we propose a non-intrusive interpretability technique that uses the input and output of the model to generate a saliency map.

28, TITLE:      JDI-T: Jointly trained Duration Informed Transformer for Text-To-Speech without Explicit Alignment
http://arxiv.org/abs/2005.07799
AUTHORS:        Dan Lim ; Won Jang ; Gyeonghwan O ; Hyeyeong Park ; Bongwan Kim ; Jesam Yoon
COMMENTS:       submitted to INTERSPEECH 2020
HIGHLIGHT:      We propose Jointly trained Duration Informed Transformer (JDI-T), a feed-forward Transformer with a duration predictor jointly trained without explicit alignments in order to generate an acoustic feature sequence from an input text.

29, TITLE:      Graph Convolutional Encoders for Syntax-aware Neural Machine Translation
http://arxiv.org/abs/1704.04675
AUTHORS:        Jasmijn Bastings ; Ivan Titov ; Wilker Aziz ; Diego Marcheggiani ; Khalil Sima'an
HIGHLIGHT:      We present a simple and effective approach to incorporating syntactic structure into neural attention-based encoder-decoder models for machine translation.

30, TITLE:      High-quality Panorama Stitching based on Asymmetric Bidirectional Optical Flow
http://arxiv.org/abs/2006.01201
AUTHORS:        Mingyuan Meng ; Shaojun Liu
COMMENTS:       5 pages, 4 figures, accepted at the 5th International Conference on Computational Intelligence and Applications (ICCIA 2020)
HIGHLIGHT:      In this paper, we propose a panorama stitching algorithm based on asymmetric bidirectional optical flow.

31, TITLE:      MultiMix: A Robust Data Augmentation Framework for Cross-Lingual NLP
http://arxiv.org/abs/2004.13240
AUTHORS:        M Saiful Bari ; Tasnim Mohiuddin ; Shafiq Joty
HIGHLIGHT:      We propose MultiMix, a novel data augmentation framework for self-supervised learning in zero-resource transfer learning scenarios.

32, TITLE:      Efficient Rollout Strategies for Bayesian Optimization
http://arxiv.org/abs/2002.10539
AUTHORS:        Eric Hans Lee ; David Eriksson ; Bolong Cheng ; Michael McCourt ; David Bindel
COMMENTS:       To appear in UAI 2020
HIGHLIGHT:      Bayesian optimization (BO) is a class of sample-efficient global optimization methods, where a probabilistic model conditioned on previous observations is used to determine future evaluations via the optimization of an acquisition function.

33, TITLE:      A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rays
http://arxiv.org/abs/2005.01578
AUTHORS:        Pedro R. A. S. Bassi ; Romis Attux
HIGHLIGHT:      Methods: We fine-tuned neural networks pretrained on ImageNet and applied a twice transfer learning approach, using NIH ChestX-Ray14 dataset as an intermediate step.

34, TITLE:      Improving Many-Objective Evolutionary Algorithms by Means of Edge-Rotated Cones
http://arxiv.org/abs/2004.06941
AUTHORS:        Yali Wang ; André Deutz ; Thomas Bäck ; Michael Emmerich
HIGHLIGHT:      As a remedy to stagnation of search in many objective optimization, in this paper, we suggest to enhance the Pareto dominance order by involving an obtuse convex dominance cone in the convergence phase of an evolutionary optimization algorithm.

35, TITLE:      On fast multiplication of a matrix by its transpose
http://arxiv.org/abs/2001.04109
AUTHORS:        Jean-Guillaume Dumas ; Clement Pernet ; Alexandre Sedoglavic
HIGHLIGHT:      We present a non-commutative algorithm for the multiplication of a 2x2-block-matrix by its transpose using 5 block products (3 recursive calls and 2 general products) over C or any finite field.We use geometric considerations on the space of bilinear forms describing 2x2 matrix products to obtain this algorithm and we show how to reduce the number of involved additions.The resulting algorithm for arbitrary dimensions is a reduction of multiplication of a matrix by its transpose to general matrix product, improving by a constant factor previously known reductions.Finally we propose schedules with low memory footprint that support a fast and memory efficient practical implementation over a finite field.To conclude, we show how to use our result in LDLT factorization.

36, TITLE:      A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction
http://arxiv.org/abs/2005.05189
AUTHORS:        Yilin Niu ; Fangkai Jiao ; Mantong Zhou ; Ting Yao ; Jingfang Xu ; Minlie Huang
COMMENTS:       12 pages, accepted by ACL 2020
HIGHLIGHT:      To address this problem, we present a Self-Training method (STM), which supervises the evidence extractor with auto-generated evidence labels in an iterative process.

37, TITLE:      Generating Positive Bounding Boxes for Balanced Training of Object Detectors
http://arxiv.org/abs/1909.09777
AUTHORS:        Kemal Oksuz ; Baris Can Cam ; Emre Akbas ; Sinan Kalkan
COMMENTS:       To appear in WACV 20
HIGHLIGHT:      We show that our pRoI generator is able to simulate other sampling methods for positive examples such as hard example mining and prime sampling.

38, TITLE:      A few filters are enough: Convolutional Neural Network for P300 Detection
http://arxiv.org/abs/1909.06970
AUTHORS:        Alicia Montserrat Alvarado-Gonzalez ; Gibran Fuentes-Pineda ; Jorge Cervantes-Ojeda
HIGHLIGHT:      In this paper, we study the performances of existing CNN architectures with diverse complexities for single-trial within-subject and cross-subject P300 detection on four different datasets.

39, TITLE:      Iterative Effect-Size Bias in Ridehailing: Measuring Social Bias in Dynamic Pricing of 100 Million Rides
http://arxiv.org/abs/2006.04599
AUTHORS:        Akshat Pandey ; Aylin Caliskan
COMMENTS:       16 pages, 6 tables, 6 figures
HIGHLIGHT:      In this work we develop a random-effects based metric for the analysis of social bias in supervised machine learning prediction models where model outputs depend on U.S. locations.

40, TITLE:      Computing syzygies in finite dimension using fast linear algebra
http://arxiv.org/abs/1912.01848
AUTHORS:        Vincent Neiger ; Éric Schost
COMMENTS:       34 pages, 7 algorithms, Journal of Complexity
HIGHLIGHT:      We consider the computation of syzygies of multivariate polynomials in a finite-dimensional setting: for a $\mathbb{K}[X_1,\dots,X_r]$-module $\mathcal{M}$ of finite dimension $D$ as a $\mathbb{K}$-vector space, and given elements $f_1,\dots,f_m$ in $\mathcal{M}$, the problem is to compute syzygies between the $f_i$'s, that is, polynomials $(p_1,\dots,p_m)$ in $\mathbb{K}[X_1,\dots,X_r]^m$ such that $p_1 f_1 + \dots + p_m f_m = 0$ in $\mathcal{M}$.

41, TITLE:      ValNorm: A New Word Embedding Intrinsic Evaluation Method Reveals Valence Biases are Consistent Across Languages and Over Decades
http://arxiv.org/abs/2006.03950
AUTHORS:        Autumn Toney ; Aylin Caliskan
COMMENTS:       16 pages, 3 figures, 11 tables
HIGHLIGHT:      By extending methods that quantify human-like biases in word embeddings, we introduce ValNorm, a new word embedding intrinsic evaluation task, and the first unsupervised method that estimates the affective meaning of valence in words with high accuracy.

42, TITLE:      On averaging the best samples in evolutionary computation
http://arxiv.org/abs/2004.11685
AUTHORS:        Laurent Meunier ; Yann Chevaleyre ; Jeremy Rapin ; Clément W. Royer ; Olivier Teytaud
HIGHLIGHT:      In the continuous unconstrained case, we prove mathematically that a single parent $\mu=1$ leads to a sub-optimal simple regret in the case of the sphere function.

43, TITLE:      Fast Mitochondria Detection for Connectomics
http://arxiv.org/abs/1812.06024
AUTHORS:        Vincent Casser ; Kai Kang ; Hanspeter Pfister ; Daniel Haehn
HIGHLIGHT:      We present a fully automatic mitochondria detector based on a modified U-Net architecture that yields high accuracy and fast processing times.

44, TITLE:      On the Predictability of Pruning Across Scales
http://arxiv.org/abs/2006.10621
AUTHORS:        Jonathan S. Rosenfeld ; Jonathan Frankle ; Michael Carbin ; Nir Shavit
HIGHLIGHT:      We show that the error of magnitude-pruned networks follows a scaling law, and that this law is of a fundamentally different nature than that of unpruned networks.

45, TITLE:      Joint Image and Depth Estimation with Mask-Based Lensless Cameras
http://arxiv.org/abs/1910.02526
AUTHORS:        Yucheng Zheng ; M. Salman Asif
COMMENTS:       Added new experiments with camera prototype
HIGHLIGHT:      In this paper, we propose a new approach for depth estimation based on an alternating gradient descent algorithm that jointly estimates a continuous depth map and light distribution of the unknown scene from its lensless measurements.

46, TITLE:      Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya
http://arxiv.org/abs/2006.07698
AUTHORS:        Abrhalei Tela ; Abraham Woubie ; Ville Hautamaki
HIGHLIGHT:      In this work, we propose a cost-effective transfer learning method to adopt a strong source language model, trained from a large monolingual corpus to a low-resource language.

47, TITLE:      Explainable and Discourse Topic-aware Neural Language Understanding
http://arxiv.org/abs/2006.10632
AUTHORS:        Yatin Chaudhary ; Hinrich Schütze ; Pankaj Gupta
COMMENTS:       Accepted at ICML2020 (13 pages, 2 figures), acknowledgements added
HIGHLIGHT:      We present a novel neural composite language model that exploits both the latent and explainable topics along with topical discourse at sentence-level in a joint learning framework of topic and language models.

48, TITLE:      Twitter discussions and emotions about COVID-19 pandemic: a machine learning approach
http://arxiv.org/abs/2005.12830
AUTHORS:        Jia Xue ; Junxiang Chen ; Ran Hu ; Chen Chen ; ChengDa Zheng ; Xiaoqian Liu ; Tingshao Zhu
HIGHLIGHT:      The objective of the study is to examine coronavirus disease (COVID-19) related discussions, concerns, and sentiments that emerged from tweets posted by Twitter users.

49, TITLE:      Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks
http://arxiv.org/abs/2003.09514
AUTHORS:        Tony C. W. Mok ; Albert C. S. Chung
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      In this paper, we present a novel, efficient unsupervised symmetric image registration method which maximizes the similarity between images within the space of diffeomorphic maps and estimates both forward and inverse transformations simultaneously.

50, TITLE:      A Morphable Face Albedo Model
http://arxiv.org/abs/2004.02711
AUTHORS:        William A. P. Smith ; Alassane Seck ; Hannah Dee ; Bernard Tiddeman ; Joshua Tenenbaum ; Bernhard Egger
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this paper, we bring together two divergent strands of research: photometric face capture and statistical 3D face appearance modelling.

51, TITLE:      Characterizing an Analogical Concept Memory for Newellian Cognitive Architectures
http://arxiv.org/abs/2006.01962
AUTHORS:        Shiwali Mohan ; Matt Klenk ; Matthew Shreve ; Kent Evans ; Aaron Ang ; John Maxwell
COMMENTS:       Under review at the Eighth Annual Conference on Advances in Cognitive Systems (ACS 2020)
HIGHLIGHT:      We propose a new long-term declarative memory for Soar that leverages the computational models of analogical reasoning and generalization.

52, TITLE:      DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
http://arxiv.org/abs/2001.00179
AUTHORS:        Ruben Tolosana ; Ruben Vera-Rodriguez ; Julian Fierrez ; Aythami Morales ; Javier Ortega-Garcia
HIGHLIGHT:      Among all the aspects discussed in the survey, we pay special attention to the latest generation of DeepFakes, highlighting its improvements and challenges for fake detection.

53, TITLE:      The eyes know it: FakeET -- An Eye-tracking Database to Understand Deepfake Perception
http://arxiv.org/abs/2006.06961
AUTHORS:        Parul Gupta ; Komal Chugh ; Abhinav Dhall ; Ramanathan Subramanian
COMMENTS:       8 pages
HIGHLIGHT:      We present \textbf{FakeET}-- an eye-tracking database to understand human visual perception of \emph{deepfake} videos.

54, TITLE:      Regression Networks for Meta-Learning Few-Shot Classification
http://arxiv.org/abs/1905.13613
AUTHORS:        Arnout Devos ; Matthias Grossglauser
COMMENTS:       7th ICML Workshop on Automated Machine Learning (2020)
HIGHLIGHT:      We propose regression networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each class.