cond-mat.stat-mech - 统计数学
cs.AI - 人工智能 cs.CL - 计算与语言 cs.CV - 机器视觉与模式识别 cs.GT - 计算机科学与博弈论 cs.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.NE - 神经与进化计算 cs.NI - 网络和互联网体系结构 cs.OH - 其他CS cs.RO - 机器人学 cs.SI - 社交网络与信息网络 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.OC - 优化与控制 math.ST - 统计理论 physics.soc-ph - 物理学与社会 quant-ph - 量子物理 stat.AP - 应用统计 stat.ME - 统计方法论 stat.ML - (统计)机器学习
• [cond-mat.stat-mech]Blind calibration for compressed sensing: State evolution and an online algorithm
• [cond-mat.stat-mech]Neural Canonical Transformation with Symplectic Flows
• [cs.AI]A note on the empirical comparison of RBG and Ludii
• [cs.AI]Distance-Based Approaches to Repair Semantics in Ontology-based Data Access
• [cs.AI]Emergent Systematic Generalization in a Situated Agent
• [cs.AI]Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks
• [cs.AI]MTab: Matching Tabular Data to Knowledge Graph using Probability Models
• [cs.AI]Reinforcement Learning for Multi-Objective Optimization of Online Decisions in High-Dimensional Systems
• [cs.AI]Synthesizing Action Sequences for Modifying Model Decisions
• [cs.AI]Towards French Smart Building Code: Compliance Checking Based on Semantic Rules
• [cs.AI]Towards Improving Solution Dominance with Incomparability Conditions: A case-study using Generator Itemset Mining
• [cs.CL]A Survey of Methods to Leverage Monolingual Data in Low-resource Neural Machine Translation
• [cs.CL]Analyzing Sentence Fusion in Abstractive Summarization
• [cs.CL]Application of Low-resource Machine Translation Techniques to Russian-Tatar Language Pair
• [cs.CL]Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models
• [cs.CL]BillSum: A Corpus for Automatic Summarization of US Legislation
• [cs.CL]Detecting Alzheimer’s Disease by estimating attention and elicitation path through the alignment of spoken picture descriptions with the picture prompt
• [cs.CL]Dialogue Transformers
• [cs.CL]Global Voices: Crossing Borders in Automatic News Summarization
• [cs.CL]Grammatical Error Correction in Low-Resource Scenarios
• [cs.CL]Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations
• [cs.CL]Interrogating the Explanatory Power of Attention in Neural Machine Translation
• [cs.CL]Latent-Variable Generative Models for Data-Efficient Text Classification
• [cs.CL]Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power
• [cs.CL]MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
• [cs.CL]Machine Translation for Machines: the Sentiment Classification Use Case
• [cs.CL]Multi-Head Attention with Diversity for Learning Grounded Multilingual Multimodal Representations
• [cs.CL]Multilingual End-to-End Speech Translation
• [cs.CL]Putting Machine Translation in Context with the Noisy Channel Model
• [cs.CL]Semantic Graph Parsing with Recurrent Neural Network DAG Grammars
• [cs.CL]Specializing Word Embeddings (for Parsing) by Information Bottleneck
• [cs.CL]TMLab: Generative Enhanced Model (GEM) for adversarial attacks
• [cs.CL]Type-aware Convolutional Neural Networks for Slot Filling
• [cs.CL]When and Why is Document-level Context Useful in Neural Machine Translation?
• [cs.CL]Writing habits and telltale neighbors: analyzing clinical concept usage patterns with sublanguage embeddings
• [cs.CV]A Three-dimensional Convolutional-Recurrent Network for Convective Storm Nowcasting
• [cs.CV]Adversarial Patches Exploiting Contextual Reasoning in Object Detection
• [cs.CV]CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule Routing
• [cs.CV]Custom Extended Sobel Filters
• [cs.CV]Deep Neural Rejection against Adversarial Examples
• [cs.CV]DenseRaC: Joint 3D Pose and Shape Estimation by Dense Render-and-Compare
• [cs.CV]End-to-end learning of energy-based representations for irregularly-sampled signals and images
• [cs.CV]Graph convolutional networks for learning with few clean and many noisy labels
• [cs.CV]Hidden Trigger Backdoor Attacks
• [cs.CV]LIP: Learning Instance Propagation for Video Object Segmentation
• [cs.CV]Real-Time Semantic Stereo Matching
• [cs.CV]Research on insect pest image detection and recognition based on bio-inspired methods
• [cs.CV]Track to Reconstruct and Reconstruct to Track
• [cs.CV]Underwhelming Generalization Improvements From Controlling Feature Attribution
• [cs.CV]Unsupervised Generative 3D Shape Learning from Natural Images
• [cs.GT]Parallel Algorithm for Approximating Nash Equilibrium in Multiplayer Stochastic Games with Application to Naval Strategic Planning
• [cs.HC]Using Conversational Agents To Support Learning By Teaching
• [cs.HC]VOnDA: A Framework for Ontology-Based Dialogue Management
• [cs.IR]Proximal Policy Optimization for Improved Convergence in IRGAN
• [cs.IT]A theoretical analysis of the error correction capability of LDPC and MDPC codes under parallel bit-flipping decoding
• [cs.IT]Impartial SWIPT-Assisted User Cooperation Schemes
• [cs.IT]Low-Resolution Limited-Feedback NOMA for mmWave Communications
• [cs.IT]Multiple Antenna Technologies for Beyond 5G
• [cs.IT]Noisy Guesses
• [cs.IT]Optimal Age-Energy Trade-off via Sleep-Wake Scheduling
• [cs.IT]Scalable String Reconciliation by Recursive Content-Dependent Shingling
• [cs.LG]A Multi-Modal Feature Embedding Approach to Diagnose Alzheimer Disease from Spoken Language
• [cs.LG]Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
• [cs.LG]Affordable Uplift: Supervised Randomization in Controlled Experiments
• [cs.LG]An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack
• [cs.LG]Augmenting learning using symmetry in a biologically-inspired domain
• [cs.LG]BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions
• [cs.LG]Blending Diverse Physical Priors with Neural Networks
• [cs.LG]Chameleon: Learning Model Initializations Across Tasks With Different Schemas
• [cs.LG]Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image Interpretation
• [cs.LG]Contextual Graph Attention for Answering Logical Queries over Incomplete Knowledge Graphs
• [cs.LG]Cross Domain Imitation Learning
• [cs.LG]Decision Explanation and Feature Importance for Invertible Networks
• [cs.LG]Deep Lifetime Clustering
• [cs.LG]Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data
• [cs.LG]Generalization in Generation: A closer look at Exposure Bias
• [cs.LG]Gradient Descent: The Ultimate Optimizer
• [cs.LG]How noise affects the Hessian spectrum in overparameterized neural networks
• [cs.LG]ISTHMUS: Secure, Scalable, Real-time and Robust Machine Learning Platform for Healthcare
• [cs.LG]Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
• [cs.LG]Leveraging Model Interpretability and Stability to increase Model Robustness
• [cs.LG]MIOpen: An Open Source Library For Deep Learning Primitives
• [cs.LG]Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights
• [cs.LG]Meta-Q-Learning
• [cs.LG]Multiagent Rollout Algorithms and Reinforcement Learning
• [cs.LG]NGEMM: Optimizing GEMM for Deep Learning via Compiler-based Techniques
• [cs.LG]Neural Embedding Propagation on Heterogeneous Networks
• [cs.LG]On the Equivalence between Node Embeddings and Structural Graph Representations
• [cs.LG]Predicting Alzheimer’s Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging
• [cs.LG]Randomized Ablation Feature Importance
• [cs.LG]Revisiting Fine-tuning for Few-shot Learning
• [cs.LG]Robust learning with the Hilbert-Schmidt independence criterion
• [cs.LG]Sampling Unknown Decision Functions to Build Classifier Copies
• [cs.LG]Sub-Architecture Ensemble Pruning in Neural Architecture Search
• [cs.LG]The Non-IID Data Quagmire of Decentralized Machine Learning
• [cs.LG]Training Generative Networks with general Optimal Transport distances
• [cs.LG]TriMap: Large-scale Dimensionality Reduction Using Triplets
• [cs.LG]Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory
• [cs.LG]Tutorial on Implied Posterior Probability for SVMs
• [cs.LG]Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health
• [cs.LG]Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference
• [cs.NE]Normalisation of Weights and Firing Rates in Spiking Neural Networks with Spike-Timing-Dependent Plasticity
• [cs.NI]Star sampling with and without replacement
• [cs.OH]Conjure Documentation, Release 2.3.0
• [cs.RO]A Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homes
• [cs.RO]An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research
• [cs.RO]Autonomous Bimanual Functional Regrasping of Novel Object Class Instances
• [cs.RO]Exploring Self-Assembling Behaviors in a Swarm of Bio-micro-robots using Surrogate-Assisted MAP-Elites
• [cs.RO]GraphRQI: Classifying Driver Behaviors Using Graph Spectrums
• [cs.RO]Manipulation Motion Taxonomy and Coding for Robots
• [cs.RO]Online Trajectory Planning Through Combined Trajectory Optimization and Function Approximation: Application to the Exoskeleton Atalante
• [cs.RO]Q-Search Trees: An Information-Theoretic Approach Towards Hierarchical Abstractions for Agents with Computational Limitations
• [cs.RO]Relational Graph Learning for Crowd Navigation
• [cs.RO]Robust Data-Driven Zero-Velocity Detection for Foot-Mounted Inertial Navigation
• [cs.SI]On the Influence of Twitter Trolls during the 2016 US Presidential Election
• [eess.AS]Additional Shared Decoder on Siamese Multi-view Encoders for Learning Acoustic Word Embeddings
• [eess.AS]Domain Expansion in DNN-based Acoustic Models for Robust Speech Recognition
• [eess.IV]Fitting IVIM with Variable Projection and Simplicial Optimization
• [eess.IV]Harmonization of diffusion MRI datasets with adaptive dictionary learning
• [eess.IV]Towards Automatic Embryo Staging in 3D+T Microscopy Images using Convolutional Neural Networks and PointNets
• [eess.IV]X-ray and Visible Spectra Circular Motion Images Dataset
• [eess.SP]A Fast and Robust Algorithm for Orientation Estimation using Inertial Sensors
• [eess.SP]Large Intelligent Surface for Positioning in Millimeter Wave MIMO Systems
• [math.OC]On the convergence of gradient descent for two layer neural networks
• [math.ST]Confidence intervals for median absolute deviations
• [math.ST]Monotonically Decreasing Sequence of Divergences
• [physics.soc-ph]Fame and Ultrafame: Measuring and comparing daily levels of `being talked about’ for United States’ presidents, their rivals, God, countries, and K-pop
• [physics.soc-ph]Joint Estimation of the Non-parametric Transitivity and Preferential Attachment Functions in Scientific Co-authorship Networks
• [quant-ph]Error Thresholds for Arbitrary Pauli Noise
• [stat.AP]Monotonic Nonparametric Dose Response Model
• [stat.AP]On choking and ingestion hazards for children in the United States
• [stat.AP]Towards Key Performance Indicators of Research Infrastructures
• [stat.AP]Usage-Based Vehicle Insurance: Driving Style Factors of Accident Probability and Severity
• [stat.AP]Verbal Autopsy in Civil Registration and Vital Statistics: The Symptom-Cause Information Archive
• [stat.ME]A random covariance model for bi-level graphical modeling with application to resting-state fMRI data
• [stat.ME]Fast and Fair Simultaneous Confidence Bands for Functional Parameters
• [stat.ME]Generalized inferential models for meta-analyses based on few studies
• [stat.ME]Point Pattern Processes and Models
• [stat.ME]Spatial methods and their applications to environmental and climate data
• [stat.ML]An Efficient Sampling Algorithm for Non-smooth Composite Potentials
• [stat.ML]Deep learning for Chemometric and non-translational data
• [stat.ML]Entropy Penalty: Towards Generalization Beyond the IID Assumption
• [stat.ML]Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings
• [stat.ML]Non-Gaussian processes and neural networks at finite widths
• [stat.ML]On the Complexity of Approximating Multimarginal Optimal Transport
• [stat.ML]Semi-supervised voice conversion with amortized variational inference
• [stat.ML]Tightening Bounds for Variational Inference by Revisiting Perturbation Theory
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• [cond-mat.stat-mech]Blind calibration for compressed sensing: State evolution and an online algorithm
Marylou Gabrié, Jean Barbier, Florent Krzakala, Lenka Zbdeborová
http://arxiv.org/abs/1910.00285v1
• [cond-mat.stat-mech]Neural Canonical Transformation with Symplectic Flows
Shuo-Hui Li, Chen-Xiao Dong, Linfeng Zhang, LeiWang
http://arxiv.org/abs/1910.00024v1
• [cs.AI]A note on the empirical comparison of RBG and Ludii
Jakub Kowalski, Maksymilian Mika, Jakub Sutowicz, Marek Szykuła
http://arxiv.org/abs/1910.00309v1
• [cs.AI]Distance-Based Approaches to Repair Semantics in Ontology-based Data Access
César Prouté, Bruno Yun, Madalina Croitoru
http://arxiv.org/abs/1910.00293v1
• [cs.AI]Emergent Systematic Generalization in a Situated Agent
Felix Hill, Andrew Lampinen, Rosalia Schneider, Stephen Clark, Matthew Botvinick, James L. McClelland, Adam Santoro
http://arxiv.org/abs/1910.00571v1
• [cs.AI]Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks
Mokanarangan Thayaparan, Marco Valentino, Viktor Schlegel, Andre Freitas
http://arxiv.org/abs/1910.00290v1
• [cs.AI]MTab: Matching Tabular Data to Knowledge Graph using Probability Models
Phuc Nguyen, Natthawut Kertkeidkachorn, Ryutaro Ichise, Hideaki Takeda
http://arxiv.org/abs/1910.00246v1
• [cs.AI]Reinforcement Learning for Multi-Objective Optimization of Online Decisions in High-Dimensional Systems
Hardik Meisheri, Vinita Baniwal, Nazneen N Sultana, Balaraman Ravindran, Harshad Khadilkar
http://arxiv.org/abs/1910.00211v1
• [cs.AI]Synthesizing Action Sequences for Modifying Model Decisions
Goutham Ramakrishnan, Yun Chan Lee, Aws Albargouthi
http://arxiv.org/abs/1910.00057v1
• [cs.AI]Towards French Smart Building Code: Compliance Checking Based on Semantic Rules
Nicolas Bus, Ana Roxin, Guillaume Picinbono, Muhammad Fahad
http://arxiv.org/abs/1910.00334v1
• [cs.AI]Towards Improving Solution Dominance with Incomparability Conditions: A case-study using Generator Itemset Mining
Gökberk Koçak, Özgür Akgün, Tias Guns, Ian Miguel
http://arxiv.org/abs/1910.00505v1
• [cs.CL]A Survey of Methods to Leverage Monolingual Data in Low-resource Neural Machine Translation
Ilshat Gibadullin, Aidar Valeev, Albina Khusainova, Adil Khan
http://arxiv.org/abs/1910.00373v1
• [cs.CL]Analyzing Sentence Fusion in Abstractive Summarization
Logan Lebanoff, John Muchovej, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, Fei Liu
http://arxiv.org/abs/1910.00203v1
• [cs.CL]Application of Low-resource Machine Translation Techniques to Russian-Tatar Language Pair
Aidar Valeev, Ilshat Gibadullin, Albina Khusainova, Adil Khan
http://arxiv.org/abs/1910.00368v1
• [cs.CL]Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models
Jeroen Van Hautte, Guy Emerson, Marek Rei
http://arxiv.org/abs/1910.00275v1
• [cs.CL]BillSum: A Corpus for Automatic Summarization of US Legislation
Anastassia Kornilova, Vlad Eidelman
http://arxiv.org/abs/1910.00523v1
• [cs.CL]Detecting Alzheimer’s Disease by estimating attention and elicitation path through the alignment of spoken picture descriptions with the picture prompt
Bahman Mirheidari, Yilin Pan, Traci Walker, Markus Reuber, Annalena Venneri, Daniel Blackburn, Heidi Christensen
http://arxiv.org/abs/1910.00515v1
• [cs.CL]Dialogue Transformers
Vladimir Vlasov, Johannes E. M. Mosig, Alan Nichol
http://arxiv.org/abs/1910.00486v1
• [cs.CL]Global Voices: Crossing Borders in Automatic News Summarization
Khanh Nguyen, Hal Daumé III
http://arxiv.org/abs/1910.00421v1
• [cs.CL]Grammatical Error Correction in Low-Resource Scenarios
Jakub Náplava, Milan Straka
http://arxiv.org/abs/1910.00353v1
• [cs.CL]Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations
Christian Hadiwinoto, Hwee Tou Ng, Wee Chung Gan
http://arxiv.org/abs/1910.00194v1
• [cs.CL]Interrogating the Explanatory Power of Attention in Neural Machine Translation
Pooya Moradi, Nishant Kambhatla, Anoop Sarkar
http://arxiv.org/abs/1910.00139v1
• [cs.CL]Latent-Variable Generative Models for Data-Efficient Text Classification
Xiaoan Ding, Kevin Gimpel
http://arxiv.org/abs/1910.00382v1
• [cs.CL]Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power
Jekaterina Novikova, Aparna Balagopalan, Ksenia Shkaruta, Frank Rudzicz
http://arxiv.org/abs/1910.00065v1
• [cs.CL]MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
Di Jin, Shuyang Gao, Jiun-Yu Kao, Tagyoung Chung, Dilek Hakkani-tur
http://arxiv.org/abs/1910.00458v1
• [cs.CL]Machine Translation for Machines: the Sentiment Classification Use Case
Amirhossein Tebbifakhr, Luisa Bentivogli, Matteo Negri, Marco Turchi
http://arxiv.org/abs/1910.00478v1
• [cs.CL]Multi-Head Attention with Diversity for Learning Grounded Multilingual Multimodal Representations
Po-Yao Huang, Xiaojun Chang, Alexander Hauptmann
http://arxiv.org/abs/1910.00058v1
• [cs.CL]Multilingual End-to-End Speech Translation
Hirofumi Inaguma, Kevin Duh, Tatsuya Kawahara, Shinji Watanabe
http://arxiv.org/abs/1910.00254v1
• [cs.CL]Putting Machine Translation in Context with the Noisy Channel Model
Lei Yu, Laurent Sartran, Wojciech Stokowiec, Wang Ling, Lingpeng Kong, Phil Blunsom, Chris Dyer
http://arxiv.org/abs/1910.00553v1
• [cs.CL]Semantic Graph Parsing with Recurrent Neural Network DAG Grammars
Federico Fancellu, Sorcha Gilroy, Adam Lopez, Mirella Lapata
http://arxiv.org/abs/1910.00051v1
• [cs.CL]Specializing Word Embeddings (for Parsing) by Information Bottleneck
Xiang Lisa Li, Jason Eisner
http://arxiv.org/abs/1910.00163v1
• [cs.CL]TMLab: Generative Enhanced Model (GEM) for adversarial attacks
Piotr Niewinski, Maria Pszona, Maria Janicka
http://arxiv.org/abs/1910.00337v1
• [cs.CL]Type-aware Convolutional Neural Networks for Slot Filling
Heike Adel, Hinrich Schütze
http://arxiv.org/abs/1910.00546v1
• [cs.CL]When and Why is Document-level Context Useful in Neural Machine Translation?
Yunsu Kim, Duc Thanh Tran, Hermann Ney
http://arxiv.org/abs/1910.00294v1
• [cs.CL]Writing habits and telltale neighbors: analyzing clinical concept usage patterns with sublanguage embeddings
Denis Newman-Griffis, Eric Fosler-Lussier
http://arxiv.org/abs/1910.00192v1
• [cs.CV]A Three-dimensional Convolutional-Recurrent Network for Convective Storm Nowcasting
Wei Zhang, Wei Li, Lei Han
http://arxiv.org/abs/1910.00527v1
• [cs.CV]Adversarial Patches Exploiting Contextual Reasoning in Object Detection
Aniruddha Saha, Akshayvarun Subramanya, Koninika Patil, Hamed Pirsiavash
http://arxiv.org/abs/1910.00068v1
• [cs.CV]CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule Routing
Kevin Duarte, Yogesh S Rawat, Mubarak Shah
http://arxiv.org/abs/1910.00132v1
• [cs.CV]Custom Extended Sobel Filters
Victor Bogdan, Cosmin Bonchiş, Ciprian Orhei
http://arxiv.org/abs/1910.00138v1
• [cs.CV]Deep Neural Rejection against Adversarial Examples
Angelo Sotgiu, Ambra Demontis, Marco Melis, Battista Biggio, Giorgio Fumera, Xiaoyi Feng, Fabio Roli
http://arxiv.org/abs/1910.00470v1
• [cs.CV]DenseRaC: Joint 3D Pose and Shape Estimation by Dense Render-and-Compare
Yuanlu Xu, Song-Chun Zhu, Tony Tung
http://arxiv.org/abs/1910.00116v1
• [cs.CV]End-to-end learning of energy-based representations for irregularly-sampled signals and images
Ronan Fablet, Lucas Drumetz, François Rousseau
http://arxiv.org/abs/1910.00556v1
• [cs.CV]Graph convolutional networks for learning with few clean and many noisy labels
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondrej Chum, Cordelia Schmid
http://arxiv.org/abs/1910.00324v1
• [cs.CV]Hidden Trigger Backdoor Attacks
Aniruddha Saha, Akshayvarun Subramanya, Hamed Pirsiavash
http://arxiv.org/abs/1910.00033v1
• [cs.CV]LIP: Learning Instance Propagation for Video Object Segmentation
Ye Lyu, George Vosselman, Gui-Song Xia, Michael Ying Yang
http://arxiv.org/abs/1910.00032v1
• [cs.CV]Real-Time Semantic Stereo Matching
Pier Luigi Dovesi, Matteo Poggi, Lorenzo Andraghetti, Miquel Martí, Hedvig Kjellström, Alessandro Pieropan, Stefano Mattoccia
http://arxiv.org/abs/1910.00541v1
• [cs.CV]Research on insect pest image detection and recognition based on bio-inspired methods
Loris Nanni, Gianluca Maguolo, Fabio Pancino
http://arxiv.org/abs/1910.00296v1
• [cs.CV]Track to Reconstruct and Reconstruct to Track
Jonathon Luiten, Tobias Fischer, Bastian Leibe
http://arxiv.org/abs/1910.00130v1
• [cs.CV]Underwhelming Generalization Improvements From Controlling Feature Attribution
Joseph D. Viviano, Becks Simpson, Francis Dutil, Yoshua Bengio, Joseph Paul Cohen
http://arxiv.org/abs/1910.00199v1
• [cs.CV]Unsupervised Generative 3D Shape Learning from Natural Images
Attila Szabó, Givi Meishvili, Paolo Favaro
http://arxiv.org/abs/1910.00287v1
• [cs.GT]Parallel Algorithm for Approximating Nash Equilibrium in Multiplayer Stochastic Games with Application to Naval Strategic Planning
Sam Ganzfried, Conner Laughlin, Charles Morefield
http://arxiv.org/abs/1910.00193v1
• [cs.HC]Using Conversational Agents To Support Learning By Teaching
Nalin Chhibber, Edith Law
http://arxiv.org/abs/1909.13443v1
• [cs.HC]VOnDA: A Framework for Ontology-Based Dialogue Management
Bernd Kiefer, Anna Welker, Christophe Biwer
http://arxiv.org/abs/1910.00340v1
• [cs.IR]Proximal Policy Optimization for Improved Convergence in IRGAN
Moksh Jain, Sowmya Kamath S
http://arxiv.org/abs/1910.00352v1
• [cs.IT]A theoretical analysis of the error correction capability of LDPC and MDPC codes under parallel bit-flipping decoding
Paolo Santini, Massimo Battaglioni, Marco Baldi, Franco Chiaraluce
http://arxiv.org/abs/1910.00472v1
• [cs.IT]Impartial SWIPT-Assisted User Cooperation Schemes
Weiyu Chen, Haiyang Ding, Shilian Wang, Daniel Benevides da Costa, Fengkui Gong
http://arxiv.org/abs/1910.00244v1
• [cs.IT]Low-Resolution Limited-Feedback NOMA for mmWave Communications
Yavuz Yapici, Ismail Guvenc, Huaiyu Dai
http://arxiv.org/abs/1910.00461v1
• [cs.IT]Multiple Antenna Technologies for Beyond 5G
Jiayi Zhang, Emil Björnson, Michail Matthaiou, Derrick Wing Kwan Ng, Hong Yang, David J. Love
http://arxiv.org/abs/1910.00092v1
• [cs.IT]Noisy Guesses
Neri Merhav
http://arxiv.org/abs/1910.00215v1
• [cs.IT]Optimal Age-Energy Trade-off via Sleep-Wake Scheduling
Ahmed M. Bedewy, Yin Sun, Rahul Singh, Ness B. Shroff
http://arxiv.org/abs/1910.00205v1
• [cs.IT]Scalable String Reconciliation by Recursive Content-Dependent Shingling
Bowen Song, Ari Trachtenberg
http://arxiv.org/abs/1910.00536v1
• [cs.LG]A Multi-Modal Feature Embedding Approach to Diagnose Alzheimer Disease from Spoken Language
S. Soroush Haj Zargarbashi, Bagher Babaali
http://arxiv.org/abs/1910.00330v1
• [cs.LG]Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, Sergey Levine
http://arxiv.org/abs/1910.00177v1
• [cs.LG]Affordable Uplift: Supervised Randomization in Controlled Experiments
Johannes Haupt, Daniel Jacob, Robin M. Gubela, Stefan Lessmann
http://arxiv.org/abs/1910.00393v1
• [cs.LG]An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack
Yang Zhang, Shiyu Chang, Mo Yu, Kaizhi Qian
http://arxiv.org/abs/1910.00511v1
• [cs.LG]Augmenting learning using symmetry in a biologically-inspired domain
Shruti Mishra, Abbas Abdolmaleki, Arthur Guez, Piotr Trochim, Doina Precup
http://arxiv.org/abs/1910.00528v1
• [cs.LG]BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions
Yatin Chaudhary, Pankaj Gupta, Hinrich Schütze
http://arxiv.org/abs/1910.00314v1
• [cs.LG]Blending Diverse Physical Priors with Neural Networks
Yunhao Ba, Guangyuan Zhao, Achuta Kadambi
http://arxiv.org/abs/1910.00201v1
• [cs.LG]Chameleon: Learning Model Initializations Across Tasks With Different Schemas
Lukas Brinkmeyer, Rafael Rego Drumond, Randolf Scholz, Josif Grabocka, Lars Schmidt-Thieme
http://arxiv.org/abs/1909.13576v2
• [cs.LG]Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image Interpretation
Ivan Donadello, Luciano Serafini
http://arxiv.org/abs/1910.00462v1
• [cs.LG]Contextual Graph Attention for Answering Logical Queries over Incomplete Knowledge Graphs
Gengchen Mai, Krzysztof Janowicz, Bo Yan, Rui Zhu, Ling Cai, Ni Lao
http://arxiv.org/abs/1910.00084v1
• [cs.LG]Cross Domain Imitation Learning
Kun Ho Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon
http://arxiv.org/abs/1910.00105v1
• [cs.LG]Decision Explanation and Feature Importance for Invertible Networks
Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li, Junlin Yang, James S. Duncan
http://arxiv.org/abs/1910.00406v1
• [cs.LG]Deep Lifetime Clustering
S Chandra Mouli, Leonardo Teixeira, Bruno Ribeiro, Jennifer Neville
http://arxiv.org/abs/1910.00547v1
• [cs.LG]Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data
Di Wang, Huanyu Zhang, Marco Gaboardi, Jinhui Xu
http://arxiv.org/abs/1910.00482v1
• [cs.LG]Generalization in Generation: A closer look at Exposure Bias
Florian Schmidt
http://arxiv.org/abs/1910.00292v1
• [cs.LG]Gradient Descent: The Ultimate Optimizer
Kartik Chandra, Erik Meijer, Samantha Andow, Emilio Arroyo-Fang, Irene Dea, Johann George, Melissa Grueter, Basil Hosmer, Steffi Stumpos, Alanna Tempest, Shannon Yang
http://arxiv.org/abs/1909.13371v1
• [cs.LG]How noise affects the Hessian spectrum in overparameterized neural networks
Mingwei Wei, David J Schwab
http://arxiv.org/abs/1910.00195v1
• [cs.LG]ISTHMUS: Secure, Scalable, Real-time and Robust Machine Learning Platform for Healthcare
Akshay Arora, Arun Nethi, Priyanka Kharat, Vency Verghese, Grant Jenkins, Steve Miff, Vikas Chowdhry, Xiao Wang
http://arxiv.org/abs/1909.13343v2
• [cs.LG]Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
Laura Rieger, Chandan Singh, W. James Murdoch, Bin Yu
http://arxiv.org/abs/1909.13584v2
• [cs.LG]Leveraging Model Interpretability and Stability to increase Model Robustness
Fei Wu, Thomas Michel, Alexandre Briot
http://arxiv.org/abs/1910.00387v1
• [cs.LG]MIOpen: An Open Source Library For Deep Learning Primitives
Jehandad Khan, Paul Fultz, Artem Tamazov, Daniel Lowell, Chao Liu, Michael Melesse, Murali Nandhimandalam, Kamil Nasyrov, Ilya Perminov, Tejash Shah, Vasilii Filippov, Jing Zhang, Jing Zhou, Bragadeesh Natarajan, Mayank Daga
http://arxiv.org/abs/1910.00078v1
• [cs.LG]Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights
Carl Yang, Yichen Feng, Pan Li, Yu Shi, Jiawei Han
http://arxiv.org/abs/1910.00004v1
• [cs.LG]Meta-Q-Learning
Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, Alexander J. Smola
http://arxiv.org/abs/1910.00125v1
• [cs.LG]Multiagent Rollout Algorithms and Reinforcement Learning
Dimitri Bertsekas
http://arxiv.org/abs/1910.00120v1
• [cs.LG]NGEMM: Optimizing GEMM for Deep Learning via Compiler-based Techniques
Wenlei Bao, Li-Wen Chang, Yang Chen, Ke Deng, Amit Agarwal, Emad Barsoum, Abe Taha
http://arxiv.org/abs/1910.00178v1
• [cs.LG]Neural Embedding Propagation on Heterogeneous Networks
Carl Yang, Jieyu Zhang, Jiawei Han
http://arxiv.org/abs/1910.00005v1
• [cs.LG]On the Equivalence between Node Embeddings and Structural Graph Representations
Balasubramaniam Srinivasan, Bruno Ribeiro
http://arxiv.org/abs/1910.00452v1
• [cs.LG]Predicting Alzheimer’s Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging
Jiaming Guo, Wei Qiu, Xiang Li, Xuandong Zhao, Ning Guo, Quanzheng Li
http://arxiv.org/abs/1910.00185v1
• [cs.LG]Randomized Ablation Feature Importance
Luke Merrick
http://arxiv.org/abs/1910.00174v1
• [cs.LG]Revisiting Fine-tuning for Few-shot Learning
Akihiro Nakamura, Tatsuya Harada
http://arxiv.org/abs/1910.00216v1
• [cs.LG]Robust learning with the Hilbert-Schmidt independence criterion
Daniel Greenfeld, Uri Shalit
http://arxiv.org/abs/1910.00270v1
• [cs.LG]Sampling Unknown Decision Functions to Build Classifier Copies
Irene Unceta, Diego Palacios, Jordi Nin, Oriol Pujol
http://arxiv.org/abs/1910.00237v1
• [cs.LG]Sub-Architecture Ensemble Pruning in Neural Architecture Search
Yijun Bian, Qingquan Song, Mengnan Du, Jun Yao, Huanhuan Chen, Xia Hu
http://arxiv.org/abs/1910.00370v1
• [cs.LG]The Non-IID Data Quagmire of Decentralized Machine Learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip B. Gibbons
http://arxiv.org/abs/1910.00189v1
• [cs.LG]Training Generative Networks with general Optimal Transport distances
Vaios Laschos, Jan Tinapp, Klaus Obermayer
http://arxiv.org/abs/1910.00535v1
• [cs.LG]TriMap: Large-scale Dimensionality Reduction Using Triplets
Ehsan Amid, Manfred K. Warmuth
http://arxiv.org/abs/1910.00204v1
• [cs.LG]Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory
Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, Tom Goldstein
http://arxiv.org/abs/1910.00359v1
• [cs.LG]Tutorial on Implied Posterior Probability for SVMs
Georgi Nalbantov, Svetoslav Ivanov
http://arxiv.org/abs/1910.00062v1
• [cs.LG]Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health
Giannis Karamanolakis, Daniel Hsu, Luis Gravano
http://arxiv.org/abs/1910.00054v1
• [cs.LG]Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference
Max-Heinrich Laves, Sontje Ihler, Karl-Philipp Kortmann, Tobias Ortmaier
http://arxiv.org/abs/1909.13550v2
• [cs.NE]Normalisation of Weights and Firing Rates in Spiking Neural Networks with Spike-Timing-Dependent Plasticity
Katarzyna Kozdon, Peter Bentley
http://arxiv.org/abs/1910.00122v1
• [cs.NI]Star sampling with and without replacement
Jonathan Stokes, Steven Weber
http://arxiv.org/abs/1910.00431v1
• [cs.OH]Conjure Documentation, Release 2.3.0
Özgür Akgün
http://arxiv.org/abs/1910.00475v1
• [cs.RO]A Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homes
Max Bajracharya, James Borders, Dan Helmick, Thomas Kollar, Michael Laskey, John Leichty, Jeremy Ma, Umashankar Nagarajan, Akiyoshi Ochiai, Josh Petersen, Krishna Shankar, Kevin Stone, Yutaka Takaoka
http://arxiv.org/abs/1910.00127v1
• [cs.RO]An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research
Felix Grimminger, Avadesh Meduri, Majid Khadiv, Julian Viereck, Manuel Wüthrich, Maximilien Naveau, Vincent Berenz, Steve Heim, Felix Widmaier, Jonathan Fiene, Alexander Badri-Spröwitz, Ludovic Righetti
http://arxiv.org/abs/1910.00093v1
• [cs.RO]Autonomous Bimanual Functional Regrasping of Novel Object Class Instances
Dmytro Pavlichenko, Diego Rodriguez, Christian Lenz, Max Schwarz, Sven Behnke
http://arxiv.org/abs/1910.00343v1
• [cs.RO]Exploring Self-Assembling Behaviors in a Swarm of Bio-micro-robots using Surrogate-Assisted MAP-Elites
Leo Cazenille, Nicolas Bredeche, Nathanael Aubert-Kato
http://arxiv.org/abs/1910.00230v1
• [cs.RO]GraphRQI: Classifying Driver Behaviors Using Graph Spectrums
Rohan Chandra, Uttaran Bhattacharya, Trisha Mittal, Xiaoyu Li, Aniket Bera, Dinesh Manocha
http://arxiv.org/abs/1910.00049v1
• [cs.RO]Manipulation Motion Taxonomy and Coding for Robots
David Paulius, Yongqiang Huang, Jason Meloncon, Yu Sun
http://arxiv.org/abs/1910.00532v1
• [cs.RO]Online Trajectory Planning Through Combined Trajectory Optimization and Function Approximation: Application to the Exoskeleton Atalante
Alexis Duburcq, Yann Chevaleyre, Nicolas Bredech, Guilhem Boéris
http://arxiv.org/abs/1910.00514v1
• [cs.RO]Q-Search Trees: An Information-Theoretic Approach Towards Hierarchical Abstractions for Agents with Computational Limitations
Daniel T. Larsson, Dipankar Maity, Panagiotis Tsiotras
http://arxiv.org/abs/1910.00063v1
• [cs.RO]Relational Graph Learning for Crowd Navigation
Changan Chen, Sha Hu, Payam Nikdel, Greg Mori, Manolis Savva
http://arxiv.org/abs/1909.13165v2
• [cs.RO]Robust Data-Driven Zero-Velocity Detection for Foot-Mounted Inertial Navigation
Brandon Wagstaff, Valentin Peretroukhin, Jonathan Kelly
http://arxiv.org/abs/1910.00529v1
• [cs.SI]On the Influence of Twitter Trolls during the 2016 US Presidential Election
Nikos Salamanos, Michael J. Jensen, Xinlei He, Yang Chen, Michael Sirivianos
http://arxiv.org/abs/1910.00531v1
• [eess.AS]Additional Shared Decoder on Siamese Multi-view Encoders for Learning Acoustic Word Embeddings
Myunghun Jung, Hyungjun Lim, Jahyun Goo, Youngmoon Jung, Hoirin Kim
http://arxiv.org/abs/1910.00341v1
• [eess.AS]Domain Expansion in DNN-based Acoustic Models for Robust Speech Recognition
Shahram Ghorbani, Soheil Khorram, John H. L. Hansen
http://arxiv.org/abs/1910.00565v1
• [eess.IV]Fitting IVIM with Variable Projection and Simplicial Optimization
Shreyas Fadnavis, Hamza Farooq, Maryam Afzali, Christoph Lenglet, Tryphon Georgiou, Hu Cheng, Sharlene Newman, Ariel Rokem, Shahnawaz Ahmed, Rafael Neto Henriques, Eric Peterson, Serge Koudoro, Eleftherios Garyfallidis
http://arxiv.org/abs/1910.00095v1
• [eess.IV]Harmonization of diffusion MRI datasets with adaptive dictionary learning
Samuel St-Jean, Max A. Viergever, Alexander Leemans
http://arxiv.org/abs/1910.00272v1
• [eess.IV]Towards Automatic Embryo Staging in 3D+T Microscopy Images using Convolutional Neural Networks and PointNets
Manuel Traub, Johannes Stegmaier
http://arxiv.org/abs/1910.00443v1
• [eess.IV]X-ray and Visible Spectra Circular Motion Images Dataset
Mikhail Chekanov, Oleg Shipitko
http://arxiv.org/abs/1909.13730v2
• [eess.SP]A Fast and Robust Algorithm for Orientation Estimation using Inertial Sensors
Manon Kok, Thomas B. Schön
http://arxiv.org/abs/1910.00463v1
• [eess.SP]Large Intelligent Surface for Positioning in Millimeter Wave MIMO Systems
Jiguang He, Henk Wymeersch, Long Kong, Olli Silvén, Markku Juntti
http://arxiv.org/abs/1910.00060v1
• [math.OC]On the convergence of gradient descent for two layer neural networks
Lei Li
http://arxiv.org/abs/1909.13671v1
• [math.ST]Confidence intervals for median absolute deviations
Chandima N. P. G. Arachchige, Luke A. Prendergast
http://arxiv.org/abs/1910.00229v1
• [math.ST]Monotonically Decreasing Sequence of Divergences
Tomohiro Nishiyama
http://arxiv.org/abs/1910.00402v1
• [physics.soc-ph]Fame and Ultrafame: Measuring and comparing daily levels of `being talked about’ for United States’ presidents, their rivals, God, countries, and K-pop
Peter Sheridan Dodds, Joshua R. Minot, Michael V. Arnold, Thayer Alshaabi, Jane Lydia Adams, David Rushing Dewhurst, Andrew J. Reagan, Christopher M. Danforth
http://arxiv.org/abs/1910.00149v1
• [physics.soc-ph]Joint Estimation of the Non-parametric Transitivity and Preferential Attachment Functions in Scientific Co-authorship Networks
Masaaki Inoue, Thong Pham, Hidetoshi Shimodaira
http://arxiv.org/abs/1910.00213v1
• [quant-ph]Error Thresholds for Arbitrary Pauli Noise
Johannes Bausch, Felix Leditzky
http://arxiv.org/abs/1910.00471v1
• [stat.AP]Monotonic Nonparametric Dose Response Model
Faten S. Alamri, Edward L. Boone, David J. Edwards
http://arxiv.org/abs/1910.00150v1
• [stat.AP]On choking and ingestion hazards for children in the United States
Frédéric Chabolle, Paul Deheuvels
http://arxiv.org/abs/1910.00310v1
• [stat.AP]Towards Key Performance Indicators of Research Infrastructures
Jana Kolar, Marjan Cugmas, Anuška Ferligoj
http://arxiv.org/abs/1910.00304v1
• [stat.AP]Usage-Based Vehicle Insurance: Driving Style Factors of Accident Probability and Severity
Konstantin Korishchenko, Ivan Stankevich, Nikolay Pilnik, Daria Marchenko
http://arxiv.org/abs/1910.00460v1
• [stat.AP]Verbal Autopsy in Civil Registration and Vital Statistics: The Symptom-Cause Information Archive
Samuel J. Clark, Philip Setel, Zehang Li
http://arxiv.org/abs/1910.00405v1
• [stat.ME]A random covariance model for bi-level graphical modeling with application to resting-state fMRI data
Lin Zhang, Andrew DiLernia, Karina Quevedo, Jazmin Camchong, Kelvin Lim, Wei Pan1
http://arxiv.org/abs/1910.00103v1
• [stat.ME]Fast and Fair Simultaneous Confidence Bands for Functional Parameters
Dominik Liebl, Matthew Reimherr
http://arxiv.org/abs/1910.00131v1
• [stat.ME]Generalized inferential models for meta-analyses based on few studies
Joyce Cahoon, Ryan Martin
http://arxiv.org/abs/1910.00533v1
• [stat.ME]Point Pattern Processes and Models
Nik Lomax, Nick Malleson, Le-Minh Kieu
http://arxiv.org/abs/1910.00282v1
• [stat.ME]Spatial methods and their applications to environmental and climate data
Behnaz Pirzamanbein
http://arxiv.org/abs/1910.00006v1
• [stat.ML]An Efficient Sampling Algorithm for Non-smooth Composite Potentials
Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, Peter L. Bartlett
http://arxiv.org/abs/1910.00551v1
• [stat.ML]Deep learning for Chemometric and non-translational data
Jacob Søgaard Larsen, Line Clemmensen
http://arxiv.org/abs/1910.00391v1
• [stat.ML]Entropy Penalty: Towards Generalization Beyond the IID Assumption
Devansh Arpit, Caiming Xiong, Richard Socher
http://arxiv.org/abs/1910.00164v1
• [stat.ML]Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings
Keith Levin, Fred Roosta, Minh Tang, Michael W. Mahoney, Carey E. Priebe
http://arxiv.org/abs/1910.00423v1
• [stat.ML]Non-Gaussian processes and neural networks at finite widths
Sho Yaida
http://arxiv.org/abs/1910.00019v1
• [stat.ML]On the Complexity of Approximating Multimarginal Optimal Transport
Tianyi Lin, Nhat Ho, Marco Cuturi, Michael I. Jordan
http://arxiv.org/abs/1910.00152v1
• [stat.ML]Semi-supervised voice conversion with amortized variational inference
Cory Stephenson, Gokce Keskin, Anil Thomas, Oguz H. Elibol
http://arxiv.org/abs/1910.00067v1
• [stat.ML]Tightening Bounds for Variational Inference by Revisiting Perturbation Theory
Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
http://arxiv.org/abs/1910.00069v1
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