机器学习每日论文速递[08.02]
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cs.LG 方向,今日共计43篇
【1】 Learning Joint Acoustic-Phonetic Word Embeddings
标题:学习联合声音词嵌入
作者: Mohamed El-Geish
链接:https://arxiv.org/abs/1908.00493
【2】 Tree-Transformer: A Transformer-Based Method for Correction of Tree-Structured Data
标题:Tree-Transformer:一种基于Transformer的树形结构数据校正方法
作者: Jacob Harer, Peter Chin
链接:https://arxiv.org/abs/1908.00449
【3】 Continual Learning via Online Leverage Score Sampling
标题:通过在线杠杆评分抽样进行持续学习
作者: Dan Teng, Sakyasingha Dasgupta
链接:https://arxiv.org/abs/1908.00355
【4】 Reinforcement Learning for Personalized Dialogue Management
标题:个性化对话管理的强化学习
作者: Floris den Hengst, Joost Bosman
链接:https://arxiv.org/abs/1908.00286
【5】 Featuring the topology with the unsupervised machine learning
标题:具有无监督机器学习的拓扑特征
作者: Kenji Fukushima, Hideaki Iida
链接:https://arxiv.org/abs/1908.00281
【6】 Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes
标题:马尔可夫决策过程中策略梯度方法的最优性和逼近
作者: Alekh Agarwal, Gaurav Mahajan
链接:https://arxiv.org/abs/1908.00261
【7】 Chainer: A Deep Learning Framework for Accelerating the Research Cycle
标题:Chainer:加速研究周期的深度学习框架
作者: Seiya Tokui, Hiroyuki Yamazaki Vincent
备注:Accepted for Applied Data Science Track in KDD'19
链接:https://arxiv.org/abs/1908.00213
【8】 Learning-Aided Physical Layer Attacks Against Multicarrier Communications in IoT
标题:物联网中针对多载波通信的学习辅助物理层攻击
作者: Alireza Nooraiepour, Narayan B. Mandayam
链接:https://arxiv.org/abs/1908.00195
【9】 Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview
标题:用于小图和巨图表示学习的图神经网络:综述
作者: Jiawei Zhang
链接:https://arxiv.org/abs/1908.00187
【10】 Accelerating CNN Training by Sparsifying Activation Gradients
标题:通过细化激活梯度加速CNN训练
作者: Xucheng Ye, Weisheng Zhao
链接:https://arxiv.org/abs/1908.00173
【11】 Deep Gaussian networks for function approximation on data defined manifolds
标题:数据定义流形上函数逼近的深高斯网络
作者: Hrushikesh Mhaskar
链接:https://arxiv.org/abs/1908.00156
【12】 Adversarial Robustness Curves
标题:对抗性稳健性曲线
作者: Christina Göpfert, Barbara Hammer
链接:https://arxiv.org/abs/1908.00096
【13】 Machine Learning at the Network Edge: A Survey
标题:网络边缘的机器学习:综述
作者: M.G. Sarwar Murshed, Faraz Hussain
链接:https://arxiv.org/abs/1908.00080
【14】 How Good is SGD with Random Shuffling?
标题:随机洗牌的SGD有多好?
作者: Itay Safran, Ohad Shamir
链接:https://arxiv.org/abs/1908.00045
【15】 Gradient Flow Algorithms for Density Propagation in Stochastic Systems
标题:随机系统密度传播的梯度流算法
作者: Kenneth F. Caluya, Abhishek Halder
链接:https://arxiv.org/abs/1908.00533
【16】 pySOT and POAP: An event-driven asynchronous framework for surrogate optimization
标题:pySOT和POAP:用于代理优化的事件驱动异步框架
作者: David Eriksson, Christine A. Shoemaker
链接:https://arxiv.org/abs/1908.00420
【17】 Sudden Death: A New Way to Compare Recommendation Diversification
标题:猝死:一种比较推荐多样化的新方法
作者: Derek Bridge, Pablo Castells
链接:https://arxiv.org/abs/1908.00419
【18】 MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation
标题:MELU:冷启动推荐的元学习用户偏好估计器
作者: Hoyeop Lee, Sehee Chung
备注:Accepted as a full paper at KDD 2019
链接:https://arxiv.org/abs/1908.00413
【19】 Extract and Merge: Merging extracted humans from different images utilizing Mask R-CNN
标题:提取和合并:利用MASK R-CNN将不同图像中提取的人类合并
作者: Asati Minkesh, Miyachi Taizo
链接:https://arxiv.org/abs/1908.00398
【20】 Classification of Cognitive Load and Expertise for Adaptive Simulation using Deep Multitask Learning
标题:使用深度多任务学习的自适应仿真认知负荷和专业知识的分类
作者: Pritam Sarkar, Ali Etemad
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/1908.00385
【21】 No-PASt-BO: Normalized Portfolio Allocation Strategy for Bayesian Optimization
标题:No-Past-BO:贝叶斯优化的规范化投资组合分配策略
作者: Thiago de P. Vasconcelos, João P. P. Gomes
链接:https://arxiv.org/abs/1908.00361
【22】 Dolphin: A Verbal Fluency Evaluation System for Elementary Education
标题:海豚:一个面向基础教育的语言流利性评价系统
作者: Zitao Liu, Gale Yan Huang
链接:https://arxiv.org/abs/1908.00358
【23】 Estimating the Standard Error of Cross-Validation-Based Estimators of Classification Rules Performance
标题:基于交叉验证的分类规则性能估计器的标准误差估计
作者: Waleed A. Yousef
链接:https://arxiv.org/abs/1908.00325
【24】 MSnet: A BERT-based Network for Gendered Pronoun Resolution
标题:MSnet:一个基于BERT的性别代词解析网络
作者: Zili Wang
备注:7 pages; 1 figures; accepted by 1st ACL Workshop on Gender Bias for NLP at ACL 2019
链接:https://arxiv.org/abs/1908.00308
【25】 Simple and Effective Text Matching with Richer Alignment Features
标题:简单有效的文本匹配,具有更丰富的对齐功能
作者: Runqi Yang, Haiqing Chen
备注:11 pages, 7 tables, 3 figures, accepted by ACL 2019
链接:https://arxiv.org/abs/1908.00300
【26】 LoadCNN: A Efficient Green Deep Learning Model for Day-ahead Individual Resident Load Forecasting
标题:LoadCNN:一种高效的提前个人居民负荷预测的绿色深度学习模型
作者: Yunyou Huang, Jianfeng Zhan
链接:https://arxiv.org/abs/1908.00298
【27】 Content and Colour Distillation for Learning Image Translations with the Spatial Profile Loss
标题:用于学习具有空间轮廓丢失的图像翻译的内容和颜色提取
作者: M. Saquib Sarfraz, Rainer Stiefelhagen
备注:BMVC 2019
链接:https://arxiv.org/abs/1908.00274
【28】 Pyramid Real Image Denoising Network
标题:金字塔实像去噪网络
作者: Yiyun Zhao, Guodong Ju
链接:https://arxiv.org/abs/1908.00273
【29】 Cultural association based on machine learning for team formation
标题:基于机器学习的团队形成文化联想
作者: Hrishikesh Kulkarni, Bradly Alicea
链接:https://arxiv.org/abs/1908.00234
【30】 Updating Variational Bayes: Fast sequential posterior inference
标题:更新变分贝叶斯:快速序贯后验推断
作者: Nathaniel Tomasetti, Anastasios Panagiotelis
链接:https://arxiv.org/abs/1908.00225
【31】 Deep Kinematic Models for Physically Realistic Prediction of Vehicle Trajectories
标题:用于物理真实预测车辆轨迹的深层运动学模型
作者: Henggang Cui, Nemanja Djuric
链接:https://arxiv.org/abs/1908.00219
【32】 Cross-domain Network Representations
标题:跨域网络表示
作者: Shan Xue, Guangquan Zhang
链接:https://arxiv.org/abs/1908.00205
【33】 KiloGrams: Very Large N-Grams for Malware Classification
标题:千克:恶意软件分类的非常大的N-gram
作者: Edward Raff, Mark McLean
备注:Appearing in LEMINCS @ KDD'19, August 5th, 2019, Anchorage, Alaska, United States
链接:https://arxiv.org/abs/1908.00200
【34】 Learning When to Drive in Intersections by Combining Reinforcement Learning and Model Predictive Control
标题:结合强化学习和模型预测控制学习交叉口何时驾驶
作者: Tommy Tram, Jonas Sjöberg
备注:6 pages, 5 figures, 1 table, Accepted to IEEE Intelligent Transport Systems Conference 2019
链接:https://arxiv.org/abs/1908.00177
【35】 FairSight: Visual Analytics for Fairness in Decision Making
标题:FairSight:决策中公平性的视觉分析
作者: Yongsu Ahn, Yu-Ru Lin
链接:https://arxiv.org/abs/1908.00176
【36】 Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results
标题:肌营养不良的多参数深度学习组织特征:初步结果
作者: Alex E. Bocchieri, Michael A. Jacobs
备注:6 pages, 3 figures. MIDL 2019 [arXiv:1907.08612]
链接:https://arxiv.org/abs/1908.00175
【37】 Personalized, Health-Aware Recipe Recommendation: An Ensemble Topic Modeling Based Approach
标题:个性化,健康意识食谱推荐:基于集成主题建模的方法
作者: Mansura A. Khan, David Coyle
备注:This is a pre-print version of the accepted full-paper in HealthRecsys2019 workshop (this https URL). The final version of the article would be published in the workshop preceding
链接:https://arxiv.org/abs/1908.00148
【38】 Few-Shot Meta-Denoising
标题:少炮元去噪
作者: Leslie Casas, Vasileios Belagiannis
链接:https://arxiv.org/abs/1908.00111
【39】 Conditional independence testing: a predictive perspective
标题:条件独立性检验:预测视角
作者: Marco Henrique de Almeida Inácio, Rafael Bassi Stern
链接:https://arxiv.org/abs/1908.00105
【40】 explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning
标题:解释器:交互式和可解释机器学习的可视化分析框架
作者: Thilo Spinner, Mennatallah El-Assady
备注:9 pages paper, 2 pages references, 5 pages supplementary material (ancillary files). Submitted and accepted for IEEE Conference on Visual Analytics Science and Technology (VAST) 2019
链接:https://arxiv.org/abs/1908.00087
【41】 Learning Effective Embeddings From Crowdsourced Labels: An Educational Case Study
标题:从众包标签学习有效嵌入:教育案例研究
作者: Guowei Xu, Zitao Liu
链接:https://arxiv.org/abs/1908.00086
【42】 Contrastive Explanations for Large Errors in Retail Forecasting Predictions through Monte Carlo Simulations
标题:蒙特卡罗模拟对零售预测大误差的对比解释
作者: Ana Lucic, Maarten de Rijke
备注:IJCAI 2019 Workshop on Explainable Artificial Intelligence
链接:https://arxiv.org/abs/1908.00085
【43】 An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation
标题:条件计算中批归一化和组归一化的实证研究
作者: Vincent Michalski, Doina Precup
链接:https://arxiv.org/abs/1908.00061
翻译:腾讯翻译君