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机器学习每日论文速递[08.06]

机器学习每日论文速递[08.06]

作者: arXiv每日论文速递 | 来源:发表于2019-08-06 10:57 被阅读0次

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    cs.LG 方向,今日共计94篇

    [cs.LG]:

    【1】 The Myths of Our Time: Fake News
    标题:我们时代的神话:假新闻
    作者: Vít Růžička, Manzil Zaheer
    备注:5 pages, 5 figures, in proceedings of International Symposium on Electronic Art 2019 (ISEA)
    链接:https://arxiv.org/abs/1908.01760

    【2】 A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning
    标题:罗生门曲线和体积的研究:机器学习中泛化和模型简化的新视角
    作者: Lesia Semenova, Cynthia Rudin
    链接:https://arxiv.org/abs/1908.01755

    【3】 Discovery of Bias and Strategic Behavior in Crowdsourced Performance Assessment
    标题:众包绩效评估中偏差和战略行为的发现
    作者: Yifei Huang, Jason Zezhong Xiao
    备注:International Workshop of Talent and Management Computing, KDD 2019
    链接:https://arxiv.org/abs/1908.01718

    【4】 Dimensionality Reduction Flows
    标题:降维流程
    作者: Hari Prasanna Das, Costas J. Spanos
    链接:https://arxiv.org/abs/1908.01686

    【5】 Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost
    标题:不平衡-XGBoost:利用二进制标签的加权和焦点损失-使用XGBoost的不平衡分类
    作者: Chen Wang, Suzhen Wang
    链接:https://arxiv.org/abs/1908.01672

    【6】 A principled approach for generating adversarial images under non-smooth dissimilarity metrics
    标题:一种在非光滑相异度度量下生成对抗性图像的原则性方法
    作者: Aram-Alexandre Pooladian, Adam Oberman
    链接:https://arxiv.org/abs/1908.01667

    【7】 Distributed Deep Convolutional Neural Networks for the Internet-of-Things
    标题:用于物联网的分布式深度卷积神经网络
    作者: Simone Disabato, Cesare Alippi
    链接:https://arxiv.org/abs/1908.01656

    【8】 Knowledge Isomorphism between Neural Networks
    标题:神经网络之间的知识同构
    作者: Ruofan Liang, Quanshi Zhang
    链接:https://arxiv.org/abs/1908.01581

    【9】 The HSIC Bottleneck: Deep Learning without Back-Propagation
    标题:HSIC瓶颈:无反向传播的深度学习
    作者: Wan-Duo Kurt Ma, W. Bastiaan Kleijn
    链接:https://arxiv.org/abs/1908.01580

    【10】 Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity Recognition
    标题:用于可解释活动识别的Deep Taylor分解中的时空相关性判别
    作者: Liam Hiley, David Marshall
    备注:5 pages, 2 figures, published at IJCAI19 ExAI workshop
    链接:https://arxiv.org/abs/1908.01536

    【11】 GAN Path Finder: Preliminary results
    标题:GaN路径查找器:初步结果
    作者: Natalia Soboleva, Konstantin Yakovlev
    备注:Camera-ready version of the paper as to appear in KI 2019 proceedings
    链接:https://arxiv.org/abs/1908.01499

    【12】 Learning to Generalize to Unseen Tasks with Bilevel Optimization
    标题:学习使用双层优化将未知任务泛化为不可见任务
    作者: Hayeon Lee, Sung Ju Hwang
    链接:https://arxiv.org/abs/1908.01457

    【13】 ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
    标题:ChemBO:具有可合成建议的小有机分子的贝叶斯优化
    作者: Ksenia Korovina, Eric P. Xing
    链接:https://arxiv.org/abs/1908.01425

    【14】 Learning to Transport with Neural Networks
    标题:用神经网络学习运输
    作者: Andrea Schioppa
    链接:https://arxiv.org/abs/1908.01394

    【15】 Simultaneous Clustering and Optimization for Evolving Datasets
    标题:进化数据集的同时聚类和优化
    作者: Yawei Zhao, Jianping Yin
    链接:https://arxiv.org/abs/1908.01384

    【16】 Semi-supervised representation learning via dual autoencoders for domain adaptation
    标题:通过用于域自适应的双自动编码器的半监督表示学习
    作者: Shuai Yang, Xuegang Hu
    链接:https://arxiv.org/abs/1908.01342

    【17】 MoGA: Searching Beyond MobileNetV3
    标题:MOGA:搜索超越MobileNetV3
    作者: Xiangxiang Chu, Ruijun Xu
    链接:https://arxiv.org/abs/1908.01314

    【18】 Building Deep, Equivariant Capsule Networks
    标题:构建深层等变胶囊网络
    作者: Sairaam Venkatraman, R. Raghunatha Sarma
    链接:https://arxiv.org/abs/1908.01300

    【19】 Dueling Posterior Sampling for Preference-Based Reinforcement Learning
    标题:基于偏好的强化学习的对决后验抽样
    作者: Ellen R. Novoseller (1), (2) Stanford University)
    链接:https://arxiv.org/abs/1908.01289

    【20】 Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network
    标题:基于知识图嵌入和卷积-LSTM网络的药物相互作用预测
    作者: Md. Rezaul Karim, Stefan Decker
    链接:https://arxiv.org/abs/1908.01288

    【21】 Deep Reinforcement Learning in System Optimization
    标题:系统优化中的深度强化学习
    作者: Ameer Haj-Ali, Ion Stoica
    链接:https://arxiv.org/abs/1908.01275

    【22】 Real-time Deep Learning at the Edge for Scalable Reliability Modeling of Si-MOSFET Power Electronics Converters
    标题:Si-MOSFET功率电子变换器可扩展可靠性建模的边缘实时深度学习
    作者: Mohammadreza Baharani, Hamed Tabkhi
    备注:2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
    链接:https://arxiv.org/abs/1908.01244

    【23】 Iterative Collaborative Filtering for Sparse Noisy Tensor Estimation
    标题:稀疏噪声张量估计的迭代协同滤波
    作者: Devavrat Shah, Christina Lee Yu
    链接:https://arxiv.org/abs/1908.01241

    【24】 Nonparametric Contextual Bandits in an Unknown Metric Space
    标题:未知度量空间中的非参数上下文带宽
    作者: Nirandika Wanigasekara, Christina Lee Yu
    链接:https://arxiv.org/abs/1908.01228

    【25】 On the Veracity of Cyber Intrusion Alerts Synthesized by Generative Adversarial Networks
    标题:生成式对抗网络合成网络入侵警报的准确性研究
    作者: Christopher Sweet, Shanchieh Jay Yang
    链接:https://arxiv.org/abs/1908.01219

    【26】 Invariance-based Adversarial Attack on Neural Machine Translation Systems
    标题:基于不变性的神经机器翻译系统的对抗攻击
    作者: Akshay Chaturvedi, Utpal Garain
    链接:https://arxiv.org/abs/1908.01165

    【27】 Local Trend Inconsistency: A Prediction-driven Approach to Unsupervised Anomaly Detection in Multi-seasonal Time Series
    标题:局部趋势不一致性:一种预测驱动的多季节时间序列非监督异常检测方法
    作者: Wentai Wu, Weiwei Lin
    链接:https://arxiv.org/abs/1908.01146

    【28】 The Use of Binary Choice Forests to Model and Estimate Discrete Choice Models
    标题:使用二元选择森林对离散选择模型进行建模和估计
    作者: Ningyuan Chen, Zhuodong Tang
    链接:https://arxiv.org/abs/1908.01109

    【29】 Toward Understanding Catastrophic Forgetting in Continual Learning
    标题:走向理解持续学习中的灾难性遗忘
    作者: Cuong V. Nguyen, Stefano Soatto
    链接:https://arxiv.org/abs/1908.01091

    【30】 Path Length Bounds for Gradient Descent and Flow
    标题:梯度下降和流动的路径长度界限
    作者: Chirag Gupta, Aaditya Ramdas
    链接:https://arxiv.org/abs/1908.01089

    【31】 Multi-label Classification for Fault Diagnosis of Rotating Electrical Machines
    标题:旋转电机故障诊断的多标签分类
    作者: Adrienn Dineva, Istvan Vajda
    链接:https://arxiv.org/abs/1908.01078

    【32】 Classi-Fly: Inferring Aircraft Categories from Open Data
    标题:Classi-Fly:从开放数据推断飞机类别
    作者: Martin Strohmeier, Ivan Martinovic
    链接:https://arxiv.org/abs/1908.01061

    【33】 Privacy-preserving Distributed Machine Learning via Local Randomization and ADMM Perturbation
    标题:基于局部随机化和ADMM扰动的隐私保护分布式机器学习
    作者: Xin Wang, Jiming Chen
    链接:https://arxiv.org/abs/1908.01059

    【34】 Weight Friction: A Simple Method to Overcome Catastrophic Forgetting and Enable Continual Learning
    标题:重量摩擦:克服灾难性遗忘和持续学习的简单方法
    作者: Gabrielle Liu
    链接:https://arxiv.org/abs/1908.01052

    【35】 Falls Prediction in eldery people using Gated Recurrent Units
    标题:使用门控循环单元的老年人跌倒预测
    作者: Marcin Radzio, Matej Mertik
    链接:https://arxiv.org/abs/1908.01050

    【36】 Linear Dynamics: Clustering without identification
    标题:线性动力学:无识别的聚类
    作者: Chloe Ching-Yun Hsu, Moritz Hardt
    链接:https://arxiv.org/abs/1908.01039

    【37】 RuleKit: A Comprehensive Suite for Rule-Based Learning
    标题:RuleKit:基于规则学习的综合套件
    作者: Adam Gudyś, Łukasz Wróbel
    链接:https://arxiv.org/abs/1908.01031

    【38】 Health-Informed Policy Gradients for Multi-Agent Reinforcement Learning
    标题:多Agent强化学习的健康信息策略梯度
    作者: Ross E. Allen, Mykel J. Kochenderfer
    链接:https://arxiv.org/abs/1908.01022

    【39】 Scalable Bayesian Non-linear Matrix Completion
    标题:可伸缩贝叶斯非线性矩阵补全
    作者: Xiangju Qin, Samuel Kaski
    备注:7 pages, 1 figures, 2 tables. The paper has been accepted for publication in the proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019)
    链接:https://arxiv.org/abs/1908.01009

    【40】 Improving Deep Reinforcement Learning in Minecraft with Action Advice
    标题:用行动建议改善《我的世界》中的深度强化学习
    作者: Spencer Frazier, Mark Riedl
    链接:https://arxiv.org/abs/1908.01007

    【41】 InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
    标题:信息图:基于互信息最大化的无监督和半监督图级表征学习
    作者: Fan-Yun Sun, Jian Tang
    链接:https://arxiv.org/abs/1908.01000

    【42】 Extending the step-size restriction for gradient descent to avoid strict saddle points
    标题:扩展梯度下降的步长限制以避免严格的鞍点
    作者: Hayden Schaeffer, Scott G. McCalla
    链接:https://arxiv.org/abs/1908.01753

    【43】 SqueezeNAS: Fast neural architecture search for faster semantic segmentation
    标题:SqueezeNAS:快速神经架构搜索,用于更快的语义分割
    作者: Albert Shaw, Sammy Sidhu
    链接:https://arxiv.org/abs/1908.01748

    【44】 Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances
    标题:用于多个问题实例上的多个算法的实验比较的样本量计算
    作者: Felipe Campelo, Elizabeth F. Wanner
    备注:31 pages. 7 Figures. Submitted to the Journal of Heuristics on 5 August 2019
    链接:https://arxiv.org/abs/1908.01720

    【45】 Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping
    标题:基于动态时间规整的时间序列相似性度量和机器学习的车削颤振检测
    作者: Melih C. Yesilli, Andreas Otto
    链接:https://arxiv.org/abs/1908.01678

    【46】 Adaptively stacking ensembles for influenza forecasting with incomplete data
    标题:不完全数据下流感预测的自适应叠加集成
    作者: Thomas McAndrew, Nicholas G. Reich
    链接:https://arxiv.org/abs/1908.01675

    【47】 Review of Algorithms for Compressive Sensing of Images
    标题:图像压缩感知算法综述
    作者: Yoni Sher
    链接:https://arxiv.org/abs/1908.01642

    【48】 Modeling Event Propagation via Graph Biased Temporal Point Process
    标题:基于图有偏时态点过程的事件传播建模
    作者: Weichang Wu, Hongyuan Zha
    链接:https://arxiv.org/abs/1908.01623

    【49】 Speech Driven Backchannel Generation using Deep Q-Network for Enhancing Engagement in Human-Robot Interaction
    标题:利用Deep Q网络生成语音驱动的反向通道以增强人机交互中的参与度
    作者: Nusrah Hussain, Yucel Yemez
    链接:https://arxiv.org/abs/1908.01618

    【50】 Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games: II -- The Finite Horizon Case
    标题:平均场控制和博弈数值解的机器学习算法收敛性分析:II-有限地平线情况
    作者: René Carmona, Mathieu Laurière
    链接:https://arxiv.org/abs/1908.01613

    【51】 Multi-Contrast Super-Resolution MRI Through a Progressive Network
    标题:基于渐进网络的多对比度超分辨率MRI
    作者: Qing Lyu, Ge Wang
    链接:https://arxiv.org/abs/1908.01612

    【52】 Solving high-dimensional optimal stopping problems using deep learning
    标题:利用深度学习求解高维最优停止问题
    作者: Sebastian Becker, Timo Welti
    链接:https://arxiv.org/abs/1908.01602

    【53】 Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content
    标题:用于在线内容情感检测的有监督机器学习技术的性能评估
    作者: Muhammad Zubair Asghar, Annamaria R. Varkonyi-Koczy
    链接:https://arxiv.org/abs/1908.01587

    【54】 Robust Over-the-Air Adversarial Examples Against Automatic Speech Recognition Systems
    标题:针对自动语音识别系统的健壮空中对抗实例
    作者: Lea Schönherr, Dorothea Kolossa
    链接:https://arxiv.org/abs/1908.01551

    【55】 A Fast Content-Based Image Retrieval Method Using Deep Visual Features
    标题:一种利用深度视觉特征的快速基于内容的图像检索方法
    作者: Hiroki Tanioka
    备注:accepted in ICDAR-WML: The 2nd International Workshop on Machine Learning 2019
    链接:https://arxiv.org/abs/1908.01505

    【56】 Imaging with highly incomplete and corrupted data
    标题:使用高度不完整和损坏的数据进行映像
    作者: Miguel Moscoso, Chrysoula Tsogka
    链接:https://arxiv.org/abs/1908.01479

    【57】 Construction of Macro Actions for Deep Reinforcement Learning
    标题:用于深度强化学习的宏动作的构建
    作者: Yi-Hsiang Chang, Chun-Yi Lee
    链接:https://arxiv.org/abs/1908.01478

    【58】 GDRQ: Group-based Distribution Reshaping for Quantization
    标题:GDRQ:用于量化的基于组的分布整形
    作者: Haibao Yu, Jianping Shi
    链接:https://arxiv.org/abs/1908.01477

    【59】 Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve
    标题:高频噪声筛分对抗实例自动检测系统
    作者: Dang Duy Thang, Toshihiro Matsui
    备注:Appear to 11th International Symposium on Cyberspace Safety and Security CSS 2019, Guangzhou, China
    链接:https://arxiv.org/abs/1908.01469

    【60】 A Deep Learning Approach for Tweet Classification and Rescue Scheduling for Effective Disaster Management
    标题:一种用于有效灾难管理的推文分类和救援调度的深度学习方法
    作者: Md. Yasin Kabir, Sanjay Madria
    链接:https://arxiv.org/abs/1908.01456

    【61】 V2S attack: building DNN-based voice conversion from automatic speaker verification
    标题:V2S攻击:从自动说话人验证建立基于DNN的语音转换
    作者: Taiki Nakamura, Hiroshi Saruwatari
    链接:https://arxiv.org/abs/1908.01454

    【62】 Sound Event Detection in Multichannel Audio using Convolutional Time-Frequency-Channel Squeeze and Excitation
    标题:利用卷积时频通道压缩和激励的多通道音频中的声音事件检测
    作者: Wei Xia, Kazuhito Koishida
    备注:Accepted by Interspeech 2019
    链接:https://arxiv.org/abs/1908.01399

    【63】 Spatio-Temporal RBF Neural Networks
    标题:时空RBF神经网络
    作者: Shujaat Khan, Muhammad Moinuddin
    备注:Published in 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST)
    链接:https://arxiv.org/abs/1908.01321

    【64】 A Dynamic Analysis of Energy Storage with Renewable and Diesel Generation using Volterra Equations
    标题:基于Volterra方程的可再生和柴油发电储能动态分析
    作者: Denis Sidorov, Aoife Foley
    链接:https://arxiv.org/abs/1908.01310

    【65】 Adversarial View-Consistent Learning for Monocular Depth Estimation
    标题:用于单目深度估计的对抗性视图一致学习
    作者: Yixuan Liu, Shengjin Wang
    备注:BMVC 2019 Spotlight
    链接:https://arxiv.org/abs/1908.01301

    【66】 The General Black-box Attack Method for Graph Neural Networks
    标题:图神经网络的一般黑盒攻击方法
    作者: Heng Chang, Junzhou Huang
    链接:https://arxiv.org/abs/1908.01297

    【67】 BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization
    标题:用于低剂量CT重建的BCD-net:加速、收敛和泛化
    作者: Il Yong Chun, Jeffrey A. Fessler
    备注:Accepted to MICCAI 2019, and the authors indicated by asterisks (*) equally contributed to this work
    链接:https://arxiv.org/abs/1908.01287

    【68】 Softmax Dissection: Towards Understanding Intra- and Inter-clas Objective for Embedding Learning
    标题:Softmax剖析:走向理解嵌入学习的班内和班际目标
    作者: Lanqing He, Shengjin Wang
    链接:https://arxiv.org/abs/1908.01281

    【69】 Hopfield Neural Network Flow: A Geometric Viewpoint
    标题:Hopfield神经网络流:一个几何观点
    作者: Abhishek Halder, Scott J. Moura
    链接:https://arxiv.org/abs/1908.01270

    【70】 A systematic review of fuzzing based on machine learning techniques
    标题:基于机器学习技术的模糊化系统综述
    作者: Yan Wang, Jiayong Liu
    链接:https://arxiv.org/abs/1908.01262

    【71】 Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models
    标题:线性模型中同时推理的收缩-扩展(MOCE)方法
    作者: Fei Wang, Peter X.-K. Song
    链接:https://arxiv.org/abs/1908.01253

    【72】 Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting
    标题:测量随机化集合的算法收敛性:回归设置
    作者: Miles E. Lopes, Thomas C. M. Lee
    链接:https://arxiv.org/abs/1908.01251

    【73】 Kannada-MNIST: A new handwritten digits dataset for the Kannada language
    标题:Kannada-MNIST:用于Kannada语言的新的手写数字数据集
    作者: Vinay Uday Prabhu
    链接:https://arxiv.org/abs/1908.01242

    【74】 Word2vec to behavior: morphology facilitates the grounding of language in machines
    标题:Word2vec to Behavior:形态学促进语言在机器中的扎根
    作者: David Matthews, Josh Bongard
    备注:D. Matthews, S. Kriegman, C. Cappelle and J. Bongard, "Word2vec to behavior: morphology facilitates the grounding of language in machines," 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 2019. \c{opyright} 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses
    链接:https://arxiv.org/abs/1908.01211

    【75】 Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
    标题:时态交互网络中动态嵌入轨迹的预测
    作者: Srijan Kumar, Jure Leskovec
    备注:ACM SIGKDD 2019 research track oral paper. The code and datasets are available on the project website: this https URL arXiv admin note: substantial text overlap with arXiv:1812.02289
    链接:https://arxiv.org/abs/1908.01207

    【76】 Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance Imaging
    标题:基于多序列磁共振成像的对抗性训练的卷积神经网络用于缺血性卒中病变的语义分割
    作者: Rachana Sathish, Debdoot Sheet
    链接:https://arxiv.org/abs/1908.01176

    【77】 Permutation-invariant Feature Restructuring for Correlation-aware Image Set-based Recognition
    标题:基于置换不变特征重构的相关感知图像集识别
    作者: Xiaofeng Liu, B.V.K. Kumar
    备注:Accepted to ICCV 2019
    链接:https://arxiv.org/abs/1908.01174

    【78】 Multiplayer Bandit Learning, from Competition to Cooperation
    标题:多人强盗学习,从竞争到合作
    作者: Simina Brânzei, Yuval Peres
    链接:https://arxiv.org/abs/1908.01135

    【79】 Machinic Surrogates: Human-Machine Relationships in Computational Creativity
    标题:机械代用品:计算创造力中的人-机关系
    作者: Ardavan Bidgoli, Daniel Cardoso Llach
    备注:25th International Symposium on Electronic Art, ISEA 2019
    链接:https://arxiv.org/abs/1908.01133

    【80】 Ensemble Neural Networks (ENN): A gradient-free stochastic method
    标题:集成神经网络(ENN):一种无梯度的随机方法
    作者: Yuntian Chena, Dongxiao Zhanga
    链接:https://arxiv.org/abs/1908.01113

    【81】 Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction
    标题:通过异常规避进行风险管理:中期股票预测的记忆深度学习
    作者: Xinyi Li, Christina Dan Wang
    链接:https://arxiv.org/abs/1908.01112

    【82】 Requirements-driven Test Generation for Autonomous Vehicles with Machine Learning Components
    标题:具有机器学习组件的自主车辆需求驱动的测试生成
    作者: Cumhur Erkan Tuncali, James Kapinski
    备注:arXiv admin note: text overlap with arXiv:1804.06760
    链接:https://arxiv.org/abs/1908.01094

    【83】 Machine-learning based three-qubit gate for realization of a Toffoli gate with cQED-based transmon systems
    标题:基于机器学习的三比特门实现基于cQED的传输子系统的Toffoli门
    作者: Sahar Daraeizadeh, Anne Y. Matsuura
    链接:https://arxiv.org/abs/1908.01092

    【84】 Network Shrinkage Estimation
    标题:网络收缩率估计
    作者: Nesreen K. Ahmed, Nick Duffield
    链接:https://arxiv.org/abs/1908.01087

    【85】 LSTM Based Music Generation System
    标题:基于LSTM的音乐生成系统
    作者: Sanidhya Mangal, Poorva Joshi
    链接:https://arxiv.org/abs/1908.01080

    【86】 U-Net Fixed-Point Quantization for Medical Image Segmentation
    标题:U-net定点量化在医学图像分割中的应用
    作者: MohammadHossein AskariHemmat, Jean-Pierre David
    备注:Accepted to MICCAI 2019's Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention
    链接:https://arxiv.org/abs/1908.01073

    【87】 On the modes of convergence of Stochastic Optimistic Mirror Descent (OMD) for saddle point problems
    标题:关于鞍点问题的随机乐观镜像下降(OMD)的收敛模式
    作者: Yanting Ma, Hassan Mansour
    链接:https://arxiv.org/abs/1908.01071

    【88】 Proposition d'un modèle pour l'optimisation automatique de boucles dans le compilateur Tiramisu : cas d'optimisation de déroulage
    标题:命题d‘un modèle Pour l’Optimalization Automatique de boucles dans le Compilateur Tiramisu:cas d‘Optimalization de déroulage
    作者: Asma Balamane, Zina Taklit
    链接:https://arxiv.org/abs/1908.01057

    【89】 Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validation
    标题:用于自主车辆验证的带有奖励增强的自适应压力测试
    作者: Anthony Corso, Mykel J. Kochenderfer
    备注:Submitted to IEEE ITSC 2019
    链接:https://arxiv.org/abs/1908.01046

    【90】 Efficient Truncated Statistics with Unknown Truncation
    标题:截断未知的有效截断统计量
    作者: Vasilis Kontonis, Manolis Zampetakis
    备注:to appear at 60th Annual IEEE Symposium on Foundations of Computer Science (FOCS), 2019
    链接:https://arxiv.org/abs/1908.01034

    【91】 Probabilistic Residual Learning for Aleatoric Uncertainty in Image Restoration
    标题:图像恢复中任意不确定性的概率残差学习
    作者: Chen Zhang, Bangti Jin
    链接:https://arxiv.org/abs/1908.01010

    【92】 Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation
    标题:用于关键点引导的图像生成的循环中循环生成对抗性网络
    作者: Hao Tang, Yan Yan
    备注:9 pages, 8 figures, accepted to ACM MM 2019
    链接:https://arxiv.org/abs/1908.00999

    【93】 Y-Net: A Hybrid Deep Learning Reconstruction Framework for Photoacoustic Imaging in vivo
    标题:Y-NET:一种用于光声成像的混合深度学习重建框架
    作者: Hengrong Lan, Fei Gao
    链接:https://arxiv.org/abs/1908.00975

    【94】 Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets
    标题:基于依赖感知的图像集无约束人脸识别注意控制
    作者: Xiaofeng Liu, Jane You
    链接:https://arxiv.org/abs/1907.03030

    翻译:腾讯翻译君

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