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机器学习每日论文速递[07.26]

机器学习每日论文速递[07.26]

作者: arXiv每日论文速递 | 来源:发表于2019-07-26 10:43 被阅读59次

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    cs.LG 方向,今日共计66篇

    [cs.LG]:

    【1】 HUGE2: a Highly Untangled Generative-model Engine for Edge-computing
    标题:HUGE2:一个用于边缘计算的高度解缠的生成模型引擎
    作者: Feng Shi, Song-Chun Zhu
    链接:https://arxiv.org/abs/1907.11210

    【2】 Prediction of Highway Lane Changes Based on Prototype Trajectories
    标题:基于原型轨迹的公路车道变化预测
    作者: David Augustin, Ulrich Konigorski
    备注:VDI AUTOREG 2019, 17 pages, 5 figures
    链接:https://arxiv.org/abs/1907.11208

    【3】 Unsupervised Domain Adaptation via Calibrating Uncertainties
    标题:通过校准不确定性的无监督域自适应
    作者: Ligong Han, Dimitris Metaxas
    链接:https://arxiv.org/abs/1907.11202

    【4】 Google Research Football: A Novel Reinforcement Learning Environment
    标题:Google Research Football:一种新的强化学习环境
    作者: Karol Kurach, Sylvain Gelly
    链接:https://arxiv.org/abs/1907.11180

    【5】 Semisupervised Adversarial Neural Networks for Cyber Security Transfer Learning
    标题:用于网络安全转移学习的半监督对抗性神经网络
    作者: Casey Kneale, Kolia Sadeghi
    链接:https://arxiv.org/abs/1907.11129

    【6】 Graph Neural Lasso for Dynamic Network Regression
    标题:动态网络回归的图神经套索
    作者: Yixin Chen, Jiawei Zhang
    链接:https://arxiv.org/abs/1907.11114

    【7】 Filter Bank Regularization of Convolutional Neural Networks
    标题:卷积神经网络的滤波器组正则化
    作者: Seyed Mehdi Ayyoubzadeh, Xiaolin Wu
    链接:https://arxiv.org/abs/1907.11110

    【8】 The Good, the Bad and the Ugly: Augmenting a black-box model with expert knowledge
    标题:好的,坏的和丑陋的:用专家知识扩充黑盒模型
    作者: Raoul Heese, Michael Bortz
    备注:International Conference on Artificial Neural Networks (ICANN) 2019
    链接:https://arxiv.org/abs/1907.11105

    【9】 Automated Discovery and Classification of Training Videos for Career Progression
    标题:职业发展培训视频的自动发现和分类
    作者: Alan Chern, Mohammed Korayem
    链接:https://arxiv.org/abs/1907.11086

    【10】 Towards Generalizing Sensorimotor Control Across Weather Conditions
    标题:向推广跨越天气条件的传感器运动控制
    作者: Qadeer Khan, Laura Leal-Taixé
    备注:Accepted for publication in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
    链接:https://arxiv.org/abs/1907.11025

    【11】 Dynamic Input for Deep Reinforcement Learning in Autonomous Driving
    标题:自主驾驶中深度强化学习的动态输入
    作者: Maria Huegle, Joschka Boedecker
    备注:Accepted at IROS 2019
    链接:https://arxiv.org/abs/1907.10994

    【12】 Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation
    标题:类不平衡下的神经网络过拟合:分割分析与改进
    作者: Zeju Li, Ben Glocker
    备注:Accepted at MICCAI 2019
    链接:https://arxiv.org/abs/1907.10982

    【13】 Learning higher-order logic programs
    标题:学习高阶逻辑程序
    作者: Andrew Cropper, Stephen H. Muggleton
    链接:https://arxiv.org/abs/1907.10953

    【14】 Logical reduction of metarules
    标题:代谢物的逻辑归约
    作者: Andrew Cropper, Sophie Tourret
    链接:https://arxiv.org/abs/1907.10952

    【15】 Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph
    标题:基于子空间并的随机几何图的谱方法理论
    作者: Gen Li, Yuantao Gu
    链接:https://arxiv.org/abs/1907.10906

    【16】 The Truly Deep Graph Convolutional Networks for Node Classification
    标题:用于节点分类的真正深图卷积网络
    作者: Yu Rong, Junzhou Huang
    链接:https://arxiv.org/abs/1907.10903

    【17】 Optuna: A Next-generation Hyperparameter Optimization Framework
    标题:Optuna:下一代超参数优化框架
    作者: Takuya Akiba, Masanori Koyama
    备注:10 pages, Accepted at KDD 2019 Applied Data Science track
    链接:https://arxiv.org/abs/1907.10902

    【18】 Simultaneous multi-view instance detection with learned geometric soft-constraints
    标题:具有学习几何软约束的同时多视图实例检测
    作者: Ahmed Samy Nassar, Jan D. Wegner
    备注:Internationcal Conference on Computer Vision 2019 (ICCV 19)
    链接:https://arxiv.org/abs/1907.10892

    【19】 Framelet Pooling Aided Deep Learning Network : The Method to Process High Dimensional Medical Data
    标题:Framelet Pooling辅助深度学习网络:处理高维医学数据的方法
    作者: Chang Min Hyun, Jin Keun Seo
    链接:https://arxiv.org/abs/1907.10834

    【20】 Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning
    标题:作为深度强化学习辅助任务的终端预测
    作者: Bilal Kartal, Matthew E. Taylor
    备注:AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE'19). arXiv admin note: text overlap with arXiv:1812.00045
    链接:https://arxiv.org/abs/1907.10827

    【21】 Enhancing Adversarial Example Transferability with an Intermediate Level Attack
    标题:通过中级攻击增强对抗实例的可转移性
    作者: Qian Huang, Ser-Nam Lim
    备注:ICCV 2019. arXiv admin note: text overlap with arXiv:1811.08458
    链接:https://arxiv.org/abs/1907.10823

    【22】 Machine learning approach to remove ion interference effect in agricultural nutrient solutions
    标题:消除农业营养液中离子干扰效应的机器学习方法
    作者: Byunghyun Ban, Minwoo Lee
    链接:https://arxiv.org/abs/1907.10794

    【23】 Towards AutoML in the presence of Drift: first results
    标题:在漂移存在的情况下走向AutoML:第一个结果
    作者: Jorge G. Madrid, Michele Sebag
    备注:AutoML 2018 @ ICML/IJCAI-ECAI
    链接:https://arxiv.org/abs/1907.10772

    【24】 Sampled Softmax with Random Fourier Features
    标题:具有随机傅立叶特征的采样Softmax
    作者: Ankit Singh Rawat, Sanjiv Kumar
    链接:https://arxiv.org/abs/1907.10747

    【25】 Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization
    标题:基于Hessian的深网SGD分析:动力学与泛化
    作者: Xinyan Li, Arindam Banerjee
    链接:https://arxiv.org/abs/1907.10732

    【26】 Automatic crack detection and classification by exploiting statistical event descriptors for Deep Learning
    标题:利用统计事件描述子进行深度学习的自动裂纹检测和分类
    作者: Giulio Siracusano, Giovanni Finocchio
    链接:https://arxiv.org/abs/1907.10709

    【27】 Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
    标题:针对深度学习对TPU、GPU和CPU平台进行基准测试
    作者: Yu (Emma) Wang, David Brooks
    链接:https://arxiv.org/abs/1907.10701

    【28】 ART: Abstraction Refinement-Guided Training for Provably Correct Neural Networks
    标题:ART:可证明正确的神经网络的抽象求精指导训练
    作者: Xuankang Lin, Suresh Jagannathan
    链接:https://arxiv.org/abs/1907.10662

    【29】 Curriculum based Dropout Discriminator for Domain Adaptation
    标题:用于领域适应的基于课程的Dropout鉴别器
    作者: Vinod Kumar Kurmi, Vinay P Namboodiri
    备注:BMVC 2019 Accepted, Project Page: this https URL
    链接:https://arxiv.org/abs/1907.10628

    【30】 Real-time Event Detection on Social Data Streams
    标题:社交数据流上的实时事件检测
    作者: Mateusz Fedoryszak, Changtao Zhong
    备注:Accepted as a full paper at KDD 2019 on April 29, 2019
    链接:https://arxiv.org/abs/1907.11229

    【31】 Multi-resolution Autoregressive Graph-to-Graph Translation for Molecules
    标题:分子的多分辨率自回归图到图的转换
    作者: Wengong Jin, Tommi Jaakkola
    链接:https://arxiv.org/abs/1907.11223

    【32】 Domain Generalization via Multidomain Discriminant Analysis
    标题:通过多领域判别分析进行领域综合
    作者: Shoubo Hu, Laiwan Chan
    备注:UAI 2019
    链接:https://arxiv.org/abs/1907.11216

    【33】 Deep Learning Models to Predict Pediatric Asthma Emergency Department Visits
    标题:深度学习模型预测小儿哮喘急诊就诊
    作者: Xiao Wang, Vikas Chowdhry
    链接:https://arxiv.org/abs/1907.11195

    【34】 HEIDL: Learning Linguistic Expressions with Deep Learning and Human-in-the-Loop
    标题:HEIDL:通过深度学习和人在环学习语言表达
    作者: Yiwei Yang, Prithviraj Sen
    链接:https://arxiv.org/abs/1907.11184

    【35】 On the bias of H-scores for comparing biclusters, and how to correct it
    标题:论H-分值对双聚类比较的偏倚及其纠正方法
    作者: Jacopo Di Iorio, Marzia A. Cremona
    链接:https://arxiv.org/abs/1907.11142

    【36】 Improving the Accuracy of Principal Component Analysis by the Maximum Entropy Method
    标题:用最大熵方法提高主成分分析的精度
    作者: Guihong Wan, Haim Schweitzer
    链接:https://arxiv.org/abs/1907.11094

    【37】 Info Intervention
    标题:信息干预
    作者: Gong Heyang, Zhu Ke
    链接:https://arxiv.org/abs/1907.11090

    【38】 The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging
    标题:虚拟膜片钳:从钙成像输入线虫膜电位
    作者: Andrew Warrington, Frank Wood
    链接:https://arxiv.org/abs/1907.11075

    【39】 DeepDrawing: A Deep Learning Approach to Graph Drawing
    标题:DeepDrawing:一种图形绘制的深度学习方法
    作者: Yong Wang, Huamin Qu
    链接:https://arxiv.org/abs/1907.11040

    【40】 Visualization of Emergency Department Clinical Data for Interpretable Patient Phenotyping
    标题:可解释患者表型的急诊科临床数据可视化
    作者: Nathan C. Hurley, Bobak J. Mortazavi
    链接:https://arxiv.org/abs/1907.11039

    【41】 Don't Worry About the Weather: Unsupervised Condition-Dependent Domain Adaptation
    标题:不要担心天气:无监督的条件依赖域适应
    作者: Horia Porav, Paul Newman
    备注:Presented at ITSC2019
    链接:https://arxiv.org/abs/1907.11004

    【42】 Personalised novel and explainable matrix factorisation
    标题:个性化的新颖和可解释的矩阵分解
    作者: Ludovik Coba, Markus Zanker
    链接:https://arxiv.org/abs/1907.11000

    【43】 Y-Autoencoders: disentangling latent representations via sequential-encoding
    标题:Y-自动编码器:通过顺序编码解除潜在表示的纠缠
    作者: Massimiliano Patacchiola, Edward Rosten
    链接:https://arxiv.org/abs/1907.10949

    【44】 Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networks
    标题:利用概率稠密位移网络缩小深度图像配准与常规图像配准之间的差距
    作者: Mattias P. Heinrich
    备注:accepted for publication at MICCAI 2019, open source code available at this https URL
    链接:https://arxiv.org/abs/1907.10931

    【45】 Self-supervised Domain Adaptation for Computer Vision Tasks
    标题:计算机视觉任务的自监督域自适应
    作者: Jiaolong Xu, Antonio M. Lopez
    链接:https://arxiv.org/abs/1907.10915

    【46】 Invariance reduces Variance: Understanding Data Augmentation in Deep Learning and Beyond
    标题:不变减少方差:理解深度学习和超越中的数据增强
    作者: Shuxiao Chen, Jane H Lee
    链接:https://arxiv.org/abs/1907.10905

    【47】 How to Manipulate CNNs to Make Them Lie: the GradCAM Case
    标题:如何操纵CNN使其说谎:GradCAM案例
    作者: Tom Viering, Elmar Eisemann
    链接:https://arxiv.org/abs/1907.10901

    【48】 Fast generalization error bound of deep learning without scale invariance of activation functions
    标题:无激活函数尺度不变性的深度学习快速泛化误差界
    作者: Yoshikazu Terada, Ryoma Hirose
    链接:https://arxiv.org/abs/1907.10900

    【49】 Adaptive Noise Injection: A Structure-Expanding Regularization for RNN
    标题:自适应噪声注入:RNN的结构扩展正则化
    作者: Rui Li, Sen Su
    链接:https://arxiv.org/abs/1907.10885

    【50】 Interpretability Beyond Classification Output: Semantic Bottleneck Networks
    标题:分类输出之外的可解释性:语义瓶颈网络
    作者: Max Losch, Bernt Schiele
    链接:https://arxiv.org/abs/1907.10882

    【51】 Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression
    标题:利用深度回归预测具有时空相关性的移动流量
    作者: Giulio Siracusano, Aurelio La Corte
    链接:https://arxiv.org/abs/1907.10865

    【52】 Learning Resolution-Invariant Deep Representations for Person Re-Identification
    标题:学习分解-用于个人重新识别的不变深度表示
    作者: Yun-Chun Chen, Yu-Chiang Frank Wang
    备注:Accepted to AAAI 2019 (Oral)
    链接:https://arxiv.org/abs/1907.10843

    【53】 Hard-Aware Fashion Attribute Classification
    标题:硬感知时尚属性分类
    作者: Yun Ye, Tao Mei
    链接:https://arxiv.org/abs/1907.10839

    【54】 Submission to ActivityNet Challenge 2019: Task B Spatio-temporal Action Localization
    标题:向ActivityNet挑战2019年提交:任务B时空操作本地化
    作者: Chunfei Ma, Seungji Yang
    链接:https://arxiv.org/abs/1907.10837

    【55】 On Mining IoT Data for Evaluating the Operation of Public Educational Buildings
    标题:公共教育建筑运营评价的物联网数据挖掘
    作者: Na Zhu, Ioannis Chatzigiannakis
    备注:13 pages, 7 figures. arXiv admin note: substantial text overlap with arXiv:1805.09561
    链接:https://arxiv.org/abs/1907.10818

    【56】 Co-Evolutionary Compression for Unpaired Image Translation
    标题:用于不配对图像平移的协同进化压缩
    作者: Han Shu, Chang Xu
    备注:Accepted by ICCV 2019
    链接:https://arxiv.org/abs/1907.10804

    【57】 Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training
    标题:使用基于特征散射的对抗训练防御对抗攻击
    作者: Haichao Zhang, Jianyu Wang
    链接:https://arxiv.org/abs/1907.10764

    【58】 One-stage Shape Instantiation from a Single 2D Image to 3D Point Cloud
    标题:从单个2D图像到3D点云的一阶段形状实例化
    作者: Xiao-Yun Zhou, Guang-Zhong Yang
    备注:8.5 pages, 5 figures, MICCAI 2019
    链接:https://arxiv.org/abs/1907.10763

    【59】 Bilingual Lexicon Induction through Unsupervised Machine Translation
    标题:基于无监督机器翻译的双语词汇归纳
    作者: Mikel Artetxe, Eneko Agirre
    备注:ACL 2019
    链接:https://arxiv.org/abs/1907.10761

    【60】 Visual Interaction with Deep Learning Models through Collaborative Semantic Inference
    标题:通过协同语义推理与深度学习模型的视觉交互
    作者: Sebastian Gehrmann, Alexander M. Rush
    备注:IEEE VIS 2019 (VAST)
    链接:https://arxiv.org/abs/1907.10739

    【61】 Careful Selection of Knowledge to solve Open Book Question Answering
    标题:精心选择知识解决开卷问答
    作者: Pratyay Banerjee, Chitta Baral
    备注:Accepted to ACL 2019
    链接:https://arxiv.org/abs/1907.10738

    【62】 Joint Adversarial Training: Incorporating both Spatial and Pixel Attacks
    标题:联合对抗训练:合并空间攻击和像素攻击
    作者: Haichao Zhang, Jianyu Wang
    链接:https://arxiv.org/abs/1907.10737

    【63】 Cross-Attention End-to-End ASR for Two-Party Conversations
    标题:两方对话的交叉关注端到端ASR
    作者: Suyoun Kim, Florian Metze
    备注:Interspeech 2019
    链接:https://arxiv.org/abs/1907.10726

    【64】 Deep Generative Quantile-Copula Models for Probabilistic Forecasting
    标题:概率预测的深生成分位数-Copula模型
    作者: Ruofeng Wen, Kari Torkkola
    备注:Published at the 36th International Conference on Machine Learning (ICML2019), Time Series Workshop, Long Beach, California, 2019
    链接:https://arxiv.org/abs/1907.10697

    【65】 Semi-parametric Object Synthesis
    标题:半参数对象综合
    作者: Andrea Palazzi, Rita Cucchiara
    链接:https://arxiv.org/abs/1907.10634

    【66】 A neural network based post-filter for speech-driven head motion synthesis
    标题:一种基于神经网络的语音驱动头部运动合成后置滤波器
    作者: JinHong Lu, Hiroshi Shimodaira
    链接:https://arxiv.org/abs/1907.10585

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