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  • 机器学习学术论文

    cs.LG 方向

    【1】 IntSGD: Floatless Compression of Stochastic Gradients
    标题:IntSGD:随机梯度的无浮点压缩
    作者:Konstantin Mishchenko,Bokun Wang,Dmitry Kovalev,Peter Richtárik
    备注:25 pages, 8 figures, 3 algorithms
    链接:https://arxiv.org/abs/2102.08374
    【2】 Analysis of feature learning in weight-tied autoencoders via the mean field lens
    标题:基于平均场透镜的加权自动编码器特征学习分析
    作者:Phan-Minh Nguyen
    机构:February
    备注:121 pages, 12 figures
    链接:https://arxiv.org/abs/2102.08373
    【3】 CTAB-GAN: Effective Table Data Synthesizing
    标题:CTAB-GAN:有效的表格数据合成
    作者:Zilong Zhao,Aditya Kunar,Hiek Van der Scheer,Robert Birke,Lydia Y. Chen
    机构:Aegon, Den Haag, Netherlands, TU Delft, Delft, Netherlands, Lydia. Chen, ABB Research Switzerland, Tu Delft, Dattwil, Switzerland
    备注:This paper consists of 11 pages which contain 8 figures, 5 tables and an appendix with a user manual for our software application. It has been submitted to KDD, 2021, Singapore
    链接:https://arxiv.org/abs/2102.08369
    【4】 COMBO: Conservative Offline Model-Based Policy Optimization
    标题:COMBO:基于保守离线模型的策略优化
    作者:Tianhe Yu,Aviral Kumar,Rafael Rafailov,Aravind Rajeswaran,Sergey Levine,Chelsea Finn
    链接:https://arxiv.org/abs/2102.08363
    【5】 A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum
    标题:平衡倒立摆的虚拟策略梯度强化学习混合方法
    作者:Dylan Bates
    机构:Center for Research in Scientific Computation North Carolina State University, Raleigh, NC, USA, RESEARCH PROBLEM, Reinforcement learning is the process of using trial-, and-error with finite rewards to encourage an agent, to take the optimal action when presented with the, state of its environment. Our agent is a single-inverted, pendulum, attached to a cart on a one-dimensional, track, with a ,-dimensional continuous state space:, [x, a, x, a] corresponding to the x-position(right, positive), angle (counterclockwise positive from , 人y, vertical), x-velocity, and angular velocity, as indicated
    备注:ICAART ’21: Proceedings of the 13th International Conference on Agents and Artificial Intelligence, Doctoral Consortium, 2021. 9 pages, 3 figures
    链接:https://arxiv.org/abs/2102.08362
    【6】 A Multi-disciplinary Ensemble Algorithm for Clustering Heterogeneous Datasets
    标题:一种异构数据集的多学科集成聚类算法
    作者:Bryar A. Hassan,Tarik A. Rashid
    机构:IKurdistan Institution for Strategic Studies and Scientific Research, Sulaimani, Iraq, University of Kurdistan Hewler, Iraq, Technical College of Informatics, Sulaimani Polytechnic University, Sulaimani, Iraq
    备注:30 pages
    链接:https://arxiv.org/abs/2102.08361
    【7】 Interpretable COVID-19 Chest X-Ray Classification via Orthogonality Constraint
    标题:基于正交性约束的可解释冠状病毒胸片分类
    作者:Ella Y. Wang,Anirudh Som,Ankita Shukla,Hongjun Choi,Pavan Turaga
    机构:BASIS Chandler, SRI International, Arizona State University
    链接:https://arxiv.org/abs/2102.08360
    【8】 Efficient Competitions and Online Learning with Strategic Forecasters
    标题:与战略预测者进行有效竞争和在线学习
    作者:Rafael Frongillo,Robert Gomez,Anish Thilagar,Bo Waggoner
    链接:https://arxiv.org/abs/2102.08358
    【9】 Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models
    标题:基于规则化和生成性的持续学习模型中的对抗性目标遗忘
    作者:Muhammad Umer,Robi Polikar
    机构:Rowan University, Glassboro, USA
    备注:arXiv admin note: text overlap with arXiv:2002.07111
    链接:https://arxiv.org/abs/2102.08355
    【10】 Topological Deep Learning: Classification Neural Networks
    标题:拓扑深度学习:分类神经网络
    作者:Mustafa Hajij,Kyle Istvan
    备注:arXiv admin note: substantial text overlap with arXiv:2008.13697
    链接:https://arxiv.org/abs/2102.08354
    【11】 Learning Invariant Representations using Inverse Contrastive Loss
    标题:利用逆对比损失学习不变表示
    作者:Aditya Kumar Akash,Vishnu Suresh Lokhande,Sathya N. Ravi,Vikas Singh
    机构: University of Wisconsin-Madison , University of Illinois at Chicago
    备注:Accepted to AAAI-21
    链接:https://arxiv.org/abs/2102.08343
    【12】 Successive Pruning for Model Compression via Rate Distortion Theory
    标题:基于率失真理论的连续剪枝模型压缩算法
    作者:Berivan Isik,Albert No,Tsachy Weissman
    备注:18 pages, 14 figures
    链接:https://arxiv.org/abs/2102.08329
    【13】 Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation
    标题:一种时间序列数据自动增强的自适应加权方案
    作者:Elizabeth Fons,Paula Dawson,Xiao-jun Zeng,John Keane,Alexandros Iosifidis
    链接:https://arxiv.org/abs/2102.08310
    【14】 Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models
    标题:基于图结构代理模型的基于模型的元强化学习
    作者:Qi Wang,Herke van Hoof
    链接:https://arxiv.org/abs/2102.08291
    【15】 Dataset Condensation with Differentiable Siamese Augmentation
    标题:基于可微暹罗增强的数据集压缩
    作者:Bo Zhao,Hakan Bilen
    链接:https://arxiv.org/abs/2102.08259
    【16】 Hierarchical VAEs Know What They Don’t Know
    标题:层次化的VAE知道他们不知道的事情
    作者:Jakob D. Havtorn,Jes Frellsen,Søren Hauberg,Lars Maaløe
    备注:18 pages, source code available at this https URL and this https URL
    链接:https://arxiv.org/abs/2102.08248
    【17】 Classification of multivariate weakly-labelled time-series with attention
    标题:带注意力的多变量弱标记时间序列的分类
    作者:Surayez Rahman,Chang Wei Tan
    链接:https://arxiv.org/abs/2102.08245
    【18】 Differentially Private Quantiles
    标题:差分私有分位数
    作者:Jennifer Gillenwater,Matthew Joseph,Alex Kulesza
    机构:February
    链接:https://arxiv.org/abs/2102.08244
    【19】 Unifying Lower Bounds on Prediction Dimension of Consistent Convex Surrogates
    标题:一致凸代理预测维数的统一下界
    作者:Jessie Finocchiaro,Rafael Frongillo,Bo Waggoner
    机构:RAFQCOLORADO.EDU, CU Boulder
    链接:https://arxiv.org/abs/2102.08218
    【20】 Improper Learning with Gradient-based Policy Optimization
    标题:基于梯度的策略优化中的不正确学习
    作者:Mohammadi Zaki,Avinash Mohan,Aditya Gopalan,Shie Mannor
    机构:Indian Institute of Science, Bengaluru, Technion, Haifa, adityadiisc. ac. in, shiedee. technion. ac.il
    链接:https://arxiv.org/abs/2102.08201
    【21】 Constructing Multiclass Classifiers using Binary Classifiers Under Log-Loss
    标题:利用二进制分类器构造原木丢失情况下的多类分类器
    作者:Assaf Ben-Yishai,Or Ordentlich
    机构:Hebrew University of Jerusalem
    备注:A shorter version of this contribution was submitted to ISIT 2021
    链接:https://arxiv.org/abs/2102.08184
    【22】 Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?
    标题:SGD中的差别隐私和拜占庭复原力:它们合起来了吗?
    作者:Rachid Guerraoui,Nirupam Gupta,Rafaël Pinot,Sébastien Rouault,John Stephan
    链接:https://arxiv.org/abs/2102.08166
    【23】 Integrating Floor Plans into Hedonic Models for Rent Price Appraisal
    标题:将平面图集成到特征模型中进行租金评估
    作者:Kirill Solovev,Nicolas Pröllochs
    机构:Nicolas Prollochs, University of Giessen, Germany
    链接:https://arxiv.org/abs/2102.08162
    【24】 RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
    标题:RMIX:协作式强化学习智能体的学习风险敏感策略
    作者:Wei Qiu,Xinrun Wang,Runsheng Yu,Xu He,Rundong Wang,Bo An,Svetlana Obraztsova,Zinovi Rabinovich
    链接:https://arxiv.org/abs/2102.08159
    【25】 Message Passing Descent for Efficient Machine Learning
    标题:用于高效机器学习的消息传递下降
    作者:Francesco Concetti,Michael Chertkov
    机构:Misha Chertkov, Program in Applied Mathematics, University of Arizona, Tucson, USA, February
    备注:15 pages, 2 figures, 6 pseudo-codes
    链接:https://arxiv.org/abs/2102.08110
    【26】 Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
    标题:匈牙利的水痘病例:利用图形神经网络进行时空信号处理的基准数据集
    作者:Benedek Rozemberczki,Paul Scherer,Oliver Kiss,Rik Sarkar,Tamas Ferenci
    机构:Dliver Kiss, The University of Edinburgh, University of Cambridge, Central European University, United Kingdom, Hungary, Obuda University; Corvinus, University of Budapest
    链接:https://arxiv.org/abs/2102.08100
    【27】 EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search
    标题:EPE-NAS:用于神经结构搜索的无需训练的高效性能估计
    作者:Vasco Lopes,Saeid Alirezazadeh,Luís A. Alexandre
    机构: NOVA LINCS, Universidade da Beira Interior, tc,-Cloud Computing Competence Center, Universidade da Beira Interior
    链接:https://arxiv.org/abs/2102.08099
    【28】 GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
    标题:GradInit:学习初始化神经网络以实现稳定高效的训练
    作者:Chen Zhu,Renkun Ni,Zheng Xu,Kezhi Kong,W. Ronny Huang,Tom Goldstein
    链接:https://arxiv.org/abs/2102.08098
    【29】 An AutoML-based Approach to Multimodal Image Sentiment Analysis
    标题:一种基于AutoML的多模态图像情感分析方法
    作者:Vasco Lopes,António Gaspar,Luís A. Alexandre,João Cordeiro
    机构:NOVA LINCS, Universidade da Beira Interior, LIAAD, INESC TECInstitute- for Systems and Computer Engineering Technology and Science, FHULTIG-Centre of Human Language Technology and Bioinformatics
    链接:https://arxiv.org/abs/2102.08092
    【30】 Orthogonal Features-based EEG Signal Denoising using Fractionally Compressed AutoEncoder
    标题:基于正交特征的分数压缩自动编码器脑电信号去噪
    作者:Subham Nagar,Ahlad Kumar,M. N. S. Swamy
    备注:9 pages, 8 figures, 26 references
    链接:https://arxiv.org/abs/2102.08083
    【31】 Steadily Learn to Drive with Virtual Memory
    标题:使用虚拟内存稳步学习驾驶
    作者:Yuhang Zhang,Yao Mu,Yujie Yang,Yang Guan,Shengbo Eben Li,Qi Sun,Jianyu Chen
    备注:Submitted to the 32nd IEEE Intelligent Vehicles Symposium
    链接:https://arxiv.org/abs/2102.08072
    【32】 Zero-Shot Adaptation for mmWave Beam-Tracking on Overhead Messenger Wires through Robust Adversarial Reinforcement Learning
    标题:基于鲁棒对抗性强化学习的架空信令线毫米波波束跟踪Zero-Shot自适应
    作者:Masao Shinzaki,Yusuke Koda,Koji Yamamoto,Takayuki Nishio,Masahiro Morikura,Yushi Shirato,Daisei Uchida,Naoki Kita
    备注:13 pages, 12 figures, 2 tables, under submission for possible publication for IEEE
    链接:https://arxiv.org/abs/2102.08055
    【33】 EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication
    标题:伊迪丝:深度学习辅助的ECG生物特征识别实现可靠的个人认证
    作者:Nabil Ibtehaz,Muhammad E. H. Chowdhury,Amith Khandakar,Serkan Kiranyaz,M. Sohel Rahman,Anas Tahir,Yazan Qiblawey,Tawsifur Rahman
    机构:University of Engineering and Technology, Dhaka-, Bangladesh, Qatar University, Doha-, University of Dhaka, February
    备注:Preprint
    链接:https://arxiv.org/abs/2102.08026
    【34】 Joint self-supervised blind denoising and noise estimation
    标题:联合自监督盲降噪和噪声估计
    作者:Jean Ollion,Charles Ollion,Elisabeth Gassiat,Luc Lehéricy,Sylvain Le Corff
    机构:SABILab, Die. sabilab. fr, CMAP, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau., Universite Paris-Saclay, CNRS, Laboratoire de mathematiques d’ Orsay, Orsay., Laboratoire J. A. Dieudonne, Universite Cote d’Azur, CNRS, Nice., +Samovar, Telecom SudParis, departement CITI, TIPIC Institut Polytechnique de Paris, Palaiseau.
    链接:https://arxiv.org/abs/2102.08023
    【35】 A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy
    标题:从4次平方和层次看图的精确推理
    作者:Kevin Bello,Chuyang Ke,Jean Honorio
    备注:15 pages, 5 figures
    链接:https://arxiv.org/abs/2102.08019
    【36】 Training Stacked Denoising Autoencoders for Representation Learning
    标题:用于表征学习的堆叠式去噪自动编码器训练
    作者:Jason Liang,Keith Kelly
    链接:https://arxiv.org/abs/2102.08012
    【37】 TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models
    标题:TeraPipe:用于大规模语言模型训练的令牌级流水线并行
    作者:Zhuohan Li,Siyuan Zhuang,Shiyuan Guo,Danyang Zhuo,Hao Zhang,Dawn Song,Ion Stoica
    链接:https://arxiv.org/abs/2102.07988
    【38】 A Generic Descent Aggregation Framework for Gradient-based Bi-level Optimization
    标题:一种通用的基于梯度的双层优化下降聚集框架
    作者:Risheng Liu,Pan Mu,Xiaoming Yuan,Shangzhi Zeng,Jin Zhang
    备注:16 pages
    链接:https://arxiv.org/abs/2102.07976
    【39】 A Sub-band Approach to Deep Denoising Wavelet Networks and a Frequency-adaptive Loss for Perceptual Quality
    标题:小波网络深度去噪的子带方法和感知质量的频率自适应损失
    作者:Caglar Aytekin,Sakari Alenius,Dmytro Paliy,Juuso Gren
    机构:AAC Technologies, Itainenkatu , Tampere
    链接:https://arxiv.org/abs/2102.07973
    【40】 Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation
    标题:基于归一化最大似然估计的离线模型优化
    作者:Justin Fu,Sergey Levine
    机构:University of California, Berkeley
    链接:https://arxiv.org/abs/2102.07970
    【41】 A Survey of Machine Learning for Computer Architecture and Systems
    标题:计算机体系结构与系统的机器学习研究综述
    作者:Nan Wu,Yuan Xie
    链接:https://arxiv.org/abs/2102.07952
    【42】 Local Hyper-flow Diffusion
    标题:局部超流扩散
    作者:Kimon Fountoulakis,Pan Li,Shenghao Yang
    机构:February
    备注:36 pages, 2 figures, 9 tables
    链接:https://arxiv.org/abs/2102.07945
    【43】 Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view
    标题:可伸缩子空间聚类的结构化图学习:从单视图到多视图
    作者:Zhao Kang,Zhiping Lin,Xiaofeng Zhu,Wenbo Xu
    链接:https://arxiv.org/abs/2102.07943
    【44】 Inverse Reinforcement Learning in the Continuous Setting with Formal Guarantees
    标题:形式保证连续环境下的逆强化学习
    作者:Gregory Dexter,Kevin Bello,Jean Honorio
    机构:Kevin bello
    链接:https://arxiv.org/abs/2102.07937
    【45】 Optimal Algorithms for Private Online Learning in a Stochastic Environment
    标题:随机环境下私人在线学习的优化算法
    作者:Bingshan Hu,Zhiming Huang,Nishant A. Mehta
    链接:https://arxiv.org/abs/2102.07929
    【46】 Improving Bayesian Inference in Deep Neural Networks with Variational Structured Dropout
    标题:变结构丢弃深度神经网络中贝叶斯推理的改进
    作者:Son Nguyen,Duong Nguyen,Khai Nguyen,Nhat Ho,Khoat Than,Hung Bui
    机构:VinAI Research, Vietnam; University of Texas, Austin;, Hanoi University of Science and Technology, February
    备注:30 pages, 5 figures
    链接:https://arxiv.org/abs/2102.07927
    【47】 Training Larger Networks for Deep Reinforcement Learning
    标题:用于深度强化学习的大型网络训练
    作者:Kei Ota,Devesh K. Jha,Asako Kanezaki
    备注:Under submission
    链接:https://arxiv.org/abs/2102.07920
    【48】 Few-Shot Graph Learning for Molecular Property Prediction
    标题:用于分子性质预测的少射图学习
    作者:Zhichun Guo,Chuxu Zhang,Wenhao Yu,John Herr,Olaf Wiest,Meng Jiang,Nitesh V. Chawla
    机构:University of Notre Dame, IN, USA ,Brandeis University, MA, USA
    备注:To appear in WWW 2021 (long paper); Code is available at this https URL
    链接:https://arxiv.org/abs/2102.07916
    【49】 Improving the Accuracy Of MEPDG Climate Modeling Using Radial Basis Function
    标题:利用径向基函数提高MEPDG气候建模精度
    作者:Amirehsan Ghasemi,Kelvin J Msechu,Arash Ghasemi,Mbakisya A. Onyango,Ignatius Fomunung,Joseph Owino
    机构:AUniversity of Tennessee at Chattanooga, Tennessee, USA
    链接:https://arxiv.org/abs/2102.07890
    【50】 Distributionally-Constrained Policy Optimization via Unbalanced Optimal Transport
    标题:基于非均衡最优运输的分布式约束策略优化
    作者:Arash Givchi,Pei Wang,Junqi Wang,Patrick Shafto
    机构:Rutgers University-Newark
    链接:https://arxiv.org/abs/2102.07889
    【51】 Momentum Residual Neural Networks
    标题:动量剩余神经网络
    作者:Michael E. Sander,Pierre Ablin,Mathieu Blondel,Gabriel Peyré
    机构:Ecole Normale Superieure, DMA, Paris, France, CNRS, France, Google Research, Brain team, February
    备注:34 pages
    链接:https://arxiv.org/abs/2102.07870
    【52】 GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
    标题:GP-Tree:一种用于小概率增量学习的高斯过程分类器
    作者:Idan Achituve,Aviv Navon,Yochai Yemini,Gal Chechik,Ethan Fetaya
    链接:https://arxiv.org/abs/2102.07868
    【53】 Unified Shapley Framework to Explain Prediction Drift
    标题:解释预测漂移的统一Shapley框架
    作者:Aalok Shanbhag,Avijit Ghosh,Josh Rubin
    链接:https://arxiv.org/abs/2102.07862
    【54】 Low Curvature Activations Reduce Overfitting in Adversarial Training
    标题:低曲率激活减少对抗性训练中的过度适应
    作者:Vasu Singla,Sahil Singla,David Jacobs,Soheil Feizi
    机构:University of Maryland University of Maryland University of Maryland
    链接:https://arxiv.org/abs/2102.07861
    【55】 KNH: Multi-View Modeling with K-Nearest Hyperplanes Graph for Misinformation Detection
    标题:KNH:用于错误信息检测的K-最近超平面图多视图建模
    作者:Sara Abdali,Neil Shah,Evangelos E. Papalexakis
    机构:University of California, Riverside, Snap Inc.
    备注:None
    链接:https://arxiv.org/abs/2102.07857
    【56】 Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification
    标题:不要只把过度自信归咎于过度参数化:二元分类校准的理论分析
    作者:Yu Bai,Song Mei,Huan Wang,Caiming Xiong
    机构:February
    链接:https://arxiv.org/abs/2102.07856
    【57】 Identifying Misinformation from Website Screenshots
    标题:从网站截图中识别错误信息
    作者:Sara Abdali,Rutuja Gurav,Siddharth Menon,Daniel Fonseca,Negin Entezari,Neil Shah,Evangelos E. Papalexakis
    机构:Snap Inc.
    备注:None
    链接:https://arxiv.org/abs/2102.07849
    【58】 MARINA: Faster Non-Convex Distributed Learning with Compression
    标题:Marina:使用压缩实现更快的非凸性分布式学习
    作者:Eduard Gorbunov,Konstantin Burlachenko,Zhize Li,Peter Richtárik
    机构: Moscow Institute of Physics and Technology, Russia, Institute for Information Transmission Problems RAS. Russia, King Abdullah University of Science and Technology, Kingdom of Saudi Arabia
    备注:48 pages, 3 figures, 3 algorithms
    链接:https://arxiv.org/abs/2102.07845
    【59】 Quartile-based Prediction of Event Types and Event Time in Business Processes using Deep Learning
    标题:基于深度学习的业务流程中事件类型和事件时间的四分位数预测
    作者:Ishwar Venugopal
    机构:University of Essex
    链接:https://arxiv.org/abs/2102.07838
    【60】 Topological Graph Neural Networks
    标题:拓扑图神经网络
    作者:Max Horn,Edward De Brouwer,Michael Moor,Yves Moreau,Bastian Rieck,Karsten Borgwardt
    机构:ETH Zurich, Basel, Switzerland, SIB Swiss Institute of Bioinformatics, Switzerland, ESAT-STADIUS, KU LEUVEN, Leuven, Belgium, These authors contributed equally., These authors jointly supervised this work.
    链接:https://arxiv.org/abs/2102.07835
    【61】 One Line To Rule Them All: Generating LO-Shot Soft-Label Prototypes
    标题:用一条线统领所有人:生成失败的软标签原型机(Lo-shot Soft-Label Prototype)
    作者:Ilia Sucholutsky,Nam-Hwui Kim,Ryan P. Browne,Matthias Schonlau
    机构:Waterloo, Canada
    备注:8 pages
    链接:https://arxiv.org/abs/2102.07834
    【62】 Phase-Modulated Radar Waveform Classification Using Deep Networks
    标题:基于深度网络的调相雷达波形分类
    作者:Michael Wharton,Anne M. Pavy,Philip Schniter
    链接:https://arxiv.org/abs/2102.07827
    【63】 A Koopman Approach to Understanding Sequence Neural Models
    标题:理解序列神经模型的库普曼方法
    作者:Ilan Naiman,Omri Azencot
    机构:Ben-Gurion University
    链接:https://arxiv.org/abs/2102.07824
    【64】 Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components
    标题:利用数据同化训练机器学习与基于知识的组件相结合的混合预报系统
    作者:Alexander Wikner,Jaideep Pathak,Brian R. Hunt,Istvan Szunyogh,Michelle Girvan,Edward Ott
    机构:University of Maryland, College Park, ) Institute for Physical Science and Technology (IPST), College Park, Texas M University, College Station, TX, ) Institute for Research in Electronics and Applied Physics (IREAP), College Park, College Park, MD , (Dated: , February ,)
    备注:28 pages, 9 figures
    链接:https://arxiv.org/abs/2102.07819
    【65】 Certified Robustness to Programmable Transformations in LSTMs
    标题:LSTM中验证的对可编程变换的健壮性
    作者:Yuhao Zhang,Aws Albarghouthi,Loris D’Antoni
    链接:https://arxiv.org/abs/2102.07818
    【66】 Online hyperparameter optimization by real-time recurrent learning
    标题:基于实时递归学习的在线超参数优化
    作者:Daniel Jiwoong Im,Cristina Savin,Kyunghyun Cho
    链接:https://arxiv.org/abs/2102.07813
    【67】 HDMI: High-order Deep Multiplex Infomax
    标题:HDMI:高阶深度复用信息传送器
    作者:Baoyu Jing,Chanyoung Park,Hanghang Tong
    机构:University of Illinois at, Urbana-Champaign, Engineering, KAIST, IL, USA, Daejeon, Republic of Korea
    备注:Accepted by WWW’2021
    链接:https://arxiv.org/abs/2102.07810
    【68】 Scaling Up Exact Neural Network Compression by ReLU Stability
    标题:基于RELU稳定性的精确神经网络放大压缩
    作者:Thiago Serra,Abhinav Kumar,Srikumar Ramalingam
    链接:https://arxiv.org/abs/2102.07804
    【69】 Efficient Learning with Arbitrary Covariate Shift
    标题:任意协变量移位的高效学习
    作者:Adam Kalai,Varun Kanade
    机构:varunkacs. ox. ac. uk, Microsoft Research New England, University of Oxford, February
    链接:https://arxiv.org/abs/2102.07802
    【70】 Online learning of Riemannian hidden Markov models in homogeneous Hadamard spaces
    标题:齐次Hadamard空间中黎曼隐马尔可夫模型的在线学习
    作者:Quinten Tupker,Salem Said,Cyrus Mostajeran
    机构:entre for Mathematical Sciences, University of Cambridge, United Kingdom, CNRS, University of Bordeaux, France
    链接:https://arxiv.org/abs/2102.07771
    【71】 Posterior-Aided Regularization for Likelihood-Free Inference
    标题:无似然推理的后验辅助正则化方法
    作者:Dongjun Kim,Kyungwoo Song,Seungjae Shin,Wanmo Kang,Il-Chul Moon
    链接:https://arxiv.org/abs/2102.07770
    【72】 Communication-Efficient Distributed Cooperative Learning with Compressed Beliefs
    标题:基于压缩信念的高效通信分布式协作学习
    作者:Mohammad Taha Toghani,Cesar A. Uribe
    机构:Rice University, Houston, TX, USA
    链接:https://arxiv.org/abs/2102.07767
    【73】 Variable importance scores
    标题:可变重要性分数
    作者:Wei-Yin Loh,Peigen Zhou
    机构:LOHQSTAT. WISC.EDU, University of Wisconsin, Madison, WI , USA, Editor:
    备注:29 pages, 13 figures
    链接:https://arxiv.org/abs/2102.07765
    【74】 Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies
    标题:通过实体感知掩蔽策略促进低资源生物医学质量保证
    作者:Gabriele Pergola,Elena Kochkina,Lin Gui,Maria Liakata,Yulan He
    机构:The Alan Turing Institute, UK
    备注:EACL 2021 – Short Paper – European Chapter of the Association for Computational Linguistics
    链接:https://arxiv.org/abs/2102.08366
    【75】 End-2-End COVID-19 Detection from Breath & Cough Audio
    标题:从呼气和咳嗽音频中检测端2端冠状病毒
    作者:Harry Coppock,Alexander Gaskell,Panagiotis Tzirakis,Alice Baird,Lyn Jones,Björn W. Schuller
    机构:Schuller, GLAM-Group on Language, Audio, Music, Imperial College London, London, UK, Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany, North Bristol NHS Trust, Bristol, UK
    备注:5 pages
    链接:https://arxiv.org/abs/2102.08359
    【76】 Revisiting Language Encoding in Learning Multilingual Representations
    标题:重新审视语言编码在学习多语言表征中的作用
    作者:Shengjie Luo,Kaiyuan Gao,Shuxin Zheng,Guolin Ke,Di He,Liwei Wang,Tie-Yan Liu
    链接:https://arxiv.org/abs/2102.08357
    【77】 Stochastic Variance Reduction for Variational Inequality Methods
    标题:变分不等式方法的随机减方差
    作者:Ahmet Alacaoglu,Yura Malitsky
    链接:https://arxiv.org/abs/2102.08352
    【78】 Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics
    标题:学习符号表达式:混合整数公式、切割和启发式
    作者:Jongeun Kim,Sven Leyffer,Prasanna Balaprakash
    链接:https://arxiv.org/abs/2102.08351
    【79】 Faster Kernel Matrix Algebra via Density Estimation
    标题:基于密度估计的快速核矩阵代数
    作者:Arturs Backurs,Piotr Indyk,Cameron Musco,Tal Wagner
    链接:https://arxiv.org/abs/2102.08341
    【80】 Context-Aware Prosody Correction for Text-Based Speech Editing
    标题:基于文本的语音编辑中的上下文感知韵律校正
    作者:Max Morrison,Lucas Rencker,Zeyu Jin,Nicholas J. Bryan,Juan-Pablo Caceres,Bryan Pardo
    机构:’Northwestern University, Evanston, IL, USA, # University of Surrey, Guildford,Uk, Adobe Research, San Francisco, CA, USA
    备注:To appear in proceedings of ICASSP 2021
    链接:https://arxiv.org/abs/2102.08328
    【81】 Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity
    标题:背包约束下的子模最大化:具有近似最优自适应复杂度的组合算法
    作者:Georgios Amanatidis,Federico Fusco,Philip Lazos,Stefano Leonardi,Alberto Marchetti Spaccamela,Rebecca Reiffenhäuser
    机构:Alberto Marchetti spaccamelat, Rebecca Reiffenhauser, February
    链接:https://arxiv.org/abs/2102.08327
    【82】 Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
    标题:共轭梯度高斯过程回归对数边际似然的更紧界
    作者:Artem Artemev,David R. Burt,Mark van der Wilk
    备注:Preprint
    链接:https://arxiv.org/abs/2102.08314
    【83】 Active Privacy-utility Trade-off Against a Hypothesis Testing Adversary
    标题:主动隐私-针对假设检验对手的效用权衡
    作者:Ecenaz Erdemir,Pier Luigi Dragotti,Deniz Gunduz
    机构:Imperial College London, UK
    备注:Accepted to IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021)
    链接:https://arxiv.org/abs/2102.08308
    【84】 A Federated Data-Driven Evolutionary Algorithm
    标题:一种联邦数据驱动的进化算法
    作者:Jinjin Xu,Yaochu Jin,Wenli Du,Sai Gu
    链接:https://arxiv.org/abs/2102.08288
    【85】 Evaluating Node Embeddings of Complex Networks
    标题:复杂网络节点嵌入度的评估
    作者:Arash Dehghan-Kooshkghazi,Bogumił Kamiński,Łukasz Kraiński,Paweł Prałat,François Théberge
    机构:Francois Theberge, February
    备注:26 pages, 18 figures
    链接:https://arxiv.org/abs/2102.08275
    【86】 An Effort to Measure Customer Relationship Performance in Indonesia’s Fintech Industry
    标题:印尼金融科技行业客户关系绩效测评研究
    作者:Alisya Putri Rabbani,Andry Alamsyah,Sri Widiyanesti
    机构: Telkom University, Bandung, Indonesia
    备注:5 pages, 2 figures, 5 tables
    链接:https://arxiv.org/abs/2102.08262
    【87】 Reinforced Contact Tracing and Epidemic Intervention
    标题:加强接触者追踪和疫情干预
    作者:Tao Feng,Sirui Song,Tong Xia,Yong Li
    链接:https://arxiv.org/abs/2102.08251
    【88】 Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models
    标题:超越显著图:训练深度模型来解释深度模型
    作者:Zixuan Liu,Ehsan Adeli,Kilian M. Pohl,Qingyu Zhao
    机构:Stanford University, CA , Center for Biomedical Sciences, SRI International, CA
    链接:https://arxiv.org/abs/2102.08239
    【89】 Modeling the Hallucinating Brain: A Generative Adversarial Framework
    标题:对产生幻觉的大脑进行建模:生成性对抗性框架
    作者:Masoumeh Zareh,Mohammad Hossein Manshaei,Sayed Jalal Zahabi
    链接:https://arxiv.org/abs/2102.08209
    【90】 Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
    标题:核条件均值嵌入和U-统计回归的条件分布处理效应
    作者:Junhyung Park,Uri Shalit,Bernhard Schölkopf,Krikamol Muandet
    机构:Max Planck Institute for Intelligent Systems, Technion, Tubingen, Germany, Israel Institute of Technology, Bernhard Scholkopf, Tubingen,Germany
    链接:https://arxiv.org/abs/2102.08208
    【91】 On Technical Trading and Social Media Indicators in Cryptocurrencies’ Price Classification Through Deep Learning
    标题:基于深度学习的加密货币价格分类中的技术交易和社交媒体指标
    作者:Marco Ortu,Nicola Uras,Claudio Conversano,Giuseppe Destefanis,Silvia Bartolucci
    机构:University of Cagliari, Brunel University, University College London, February
    链接:https://arxiv.org/abs/2102.08189
    【92】 Improving Deep-learning-based Semi-supervised Audio Tagging with Mixup
    标题:利用MIXUP改进基于深度学习的半监督音频标注
    作者:Léo Cances,Etienne Labbé,Thomas Pellegrini
    机构:IRIT, Universite Paul Sabatier, CNRS, Toulouse, France
    备注:9 pages, 5 figures, 5 tables
    链接:https://arxiv.org/abs/2102.08183
    【93】 Differentiating Surgeon Expertise Solely by Eye Movement Features
    标题:仅凭眼动特征来区分外科医生的专业知识
    作者:Benedikt Hosp,Myat Su Yin,Peter Haddawy,Paphon Sa-Ngasoongsong,Enkelejda Kasneci
    机构:Solely by Eye Movement Features. In Woodstock ‘,: ACM Symposium on Neural Gaze Detection, June ,-, Woodstock, NY.
    链接:https://arxiv.org/abs/2102.08155
    【94】 End-to-End Automatic Speech Recognition with Deep Mutual Learning
    标题:具有深度交互学习的端到端自动语音识别
    作者:Ryo Masumura,Mana Ihori,Akihiko Takashima,Tomohiro Tanaka,Takanori Ashihara
    机构: NTT Media Intelligence Laboratories, NTT Corporation, Japan
    备注:Accepted at Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2020, pp.632-637
    链接:https://arxiv.org/abs/2102.08154
    【95】 Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels
    标题:Flow-Mixup:标签损坏的多标签医学图像分类
    作者:Jintai Chen,Hongyun Yu,Ruiwei Feng,Danny Z. Chen,Jian Wu
    机构:College of Computer Science and Technology, Zhejiang University, Hangzhou, China, Danny. Chen, University of Notre Dame, Hangzhou,China, Notre Dame, IN , USA
    备注:None
    链接:https://arxiv.org/abs/2102.08148
    【96】 Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents
    标题:大语境会话表征学习:会话文档的自我监督学习
    作者:Ryo Masumura,Naoki Makishima,Mana Ihori,Akihiko Takashima,Tomohiro Tanaka,Shota Orihashi
    机构:NTT Media Intelligence Laboratories, NTT Corporation, Japan
    备注:Accepted at IEEE Spoken Language Technology Workshop (SLT), 2021, pp.1012-1019
    链接:https://arxiv.org/abs/2102.08147
    【97】 IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning
    标题:IRONMAN:基于强化学习的GNN辅助高级综合设计空间探索
    作者:Nan Wu,Yuan Xie,Cong Hao
    机构:University of California, Santa Barbara, Georgia Institute of Technology, Santa Barbara, CA, USA, Atlanta,GA, USA
    链接:https://arxiv.org/abs/2102.08138
    【98】 Spatio-Temporal Multi-step Prediction of Influenza Outbreaks
    标题:流感暴发的时空多步预测
    作者:Jie Zhang,Kazumitsu Nawata,Hongyan Wu
    机构:Joint Engineering Research
    链接:https://arxiv.org/abs/2102.08137
    【99】 Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model
    标题:使用师生模型捕获真实数据集的通用要素地图的学习曲线
    作者:Bruno Loureiro,Cédric Gerbelot,Hugo Cui,Sebastian Goldt,Florent Krzakala,Marc Mézard,Lenka Zdeborová
    机构:IdePHICS laboratory, Ecole Federale Polytechnique de Lausanne (EPFL), Switzerland, Laboratoire de Physique de ,’Ecole Normale Superieure, Universite PSL, CNRS, Sorbonne Universite, Universite Paris-Diderot, Sorbonne Paris Cite, Paris, France, SPOC laboratory, Ecole Federale Polytechnique de Lausanne (EPFL), Switzerland, Trieste, Italy
    备注:main: 13 pages, 5 figures; appendix: 52 pages, 4 figures
    链接:https://arxiv.org/abs/2102.08127
    【100】 Simple statistical models and sequential deep learning for Lithium-ion batteries degradation under dynamic conditions: Fractional Polynomials vs Neural Networks
    标题:动态条件下锂离子电池退化的简单统计模型和序贯深度学习:分数多项式与神经网络
    作者:Clara B. Salucci,Azzeddine Bakdi,Ingrid K. Glad,Erik Vanem,Riccardo De Bin
    机构:February
    备注:42 pages, 7 figures, submitted to Journal of Power Sources
    链接:https://arxiv.org/abs/2102.08111
    【101】 Composing Pick-and-Place Tasks By Grounding Language
    标题:用扎根的语言构思挑挑拣拣的任务
    作者:Oier Mees,Wolfram Burgard
    备注:Accepted at the International Symposium on Experimental Robotics (ISER) 2020. Videos at this http URL
    链接:https://arxiv.org/abs/2102.08094
    【102】 A Law of Robustness for Weight-bounded Neural Networks
    标题:权值有界神经网络的一条鲁棒性定律
    作者:Hisham Husain,Borja Balle
    机构:The australian National University Data, DeepMind
    链接:https://arxiv.org/abs/2102.08093
    【103】 Making the most of your day: online learning for optimal allocation of time
    标题:充分利用您的一天:在线学习,优化时间分配
    作者:Etienne Boursier,Tristan Garrec,Vianney Perchet,Marco Scarsini
    链接:https://arxiv.org/abs/2102.08087
    【104】 Boosting Deep Transfer Learning for COVID-19 Classification
    标题:增强深度迁移学习在冠状病毒分类中的应用
    作者:Fouzia Altaf,Syed M. S. Islam,Naeem K. Janjua,Naveed Akhtar
    机构:Edith Cowan University, University of Western Australia.
    备注:5 pages
    链接:https://arxiv.org/abs/2102.08085
    【105】 Learning the Noise of Failure: Intelligent System Tests for Robots
    标题:学习失败的噪音:机器人的智能系统测试
    作者:Felix Sygulla,Daniel Rixen
    链接:https://arxiv.org/abs/2102.08080
    【106】 Axial Residual Networks for CycleGAN-based Voice Conversion
    标题:基于CycleGAN的语音转换的轴向残差网络
    作者:Jaeseong You,Gyuhyeon Nam,Dalhyun Kim,Gyeongsu Chae
    机构:Money Brain Inc.
    链接:https://arxiv.org/abs/2102.08075
    【107】 Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining
    标题:基于情景三元组挖掘的半监督学习在Few-Shot音频分类中的应用
    作者:Swapnil Bhosale,Rupayan Chakraborty,Sunil Kumar Kopparapu
    机构:TCS Research and Innovation-Mumbai, INDIA
    备注:5 pages
    链接:https://arxiv.org/abs/2102.08074
    【108】 Exploring Transformers in Natural Language Generation: GPT, BERT, and XLNet
    标题:探索自然语言生成中的转换器:GPT、BERT和XLNet
    作者:M. Onat Topal,Anil Bas,Imke van Heerden
    机构:Dept. of Computer Engineering, Faculty, Dept. of Comparative Literature, Middle East Technical University, of Technology, Marmara University, CSSH, Kog University, Ankara, Turkey, Istanbul, Turkey
    备注:Accepted as oral presentation to ICIDAAI 2021 – Short Paper
    链接:https://arxiv.org/abs/2102.08036
    【109】 Enhancing Hierarchical Information by Using Metric Cones for Graph Embedding
    标题:利用度量圆锥增强图嵌入的层次信息
    作者:Daisuke Takehara,Kei Kobayashi
    链接:https://arxiv.org/abs/2102.08014
    【110】 Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking
    标题:用于以太事务跟踪的时量快照多重图
    作者:Yunyi Xie,Jie Jin,Jian Zhang,Shanqing Yu,Qi Xuan
    机构: Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou , China, College of Information Engineering, Zhejiang University of Technology, PCL Research Center of Networks and Communications, Peng Cheng Laboratory, Shenzhen ,China
    链接:https://arxiv.org/abs/2102.08013
    【111】 EfficientLPS: Efficient LiDAR Panoptic Segmentation
    标题:高效LPS:高效的LiDAR全景图像分割
    作者:Kshitij Sirohi,Rohit Mohan,Daniel Büscher,Wolfram Burgard,Abhinav Valada
    备注:Ranked #1 on SemanticKITTI panoptic segmentation benchmark
    链接:https://arxiv.org/abs/2102.08009
    【112】 D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis
    标题:D2A:一个基于差分分析的基于人工智能漏洞检测方法的数据集
    作者:Yunhui Zheng,Saurabh Pujar,Burn Lewis,Luca Buratti,Edward Epstein,Bo Yang,Jim Laredo,Alessandro Morari,Zhong Su
    机构:IBM Research
    备注:Accepted to the 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP ’21)
    链接:https://arxiv.org/abs/2102.07995
    【113】 The Randomized Elliptical Potential Lemma with an Application to Linear Thompson Sampling
    标题:随机椭圆势引理及其在线性Thompson抽样中的应用
    作者:Nima Hamidi,Mohsen Bayati
    机构:February
    链接:https://arxiv.org/abs/2102.07987
    【114】 Twin Augmented Architectures for Robust Classification of COVID-19 Chest X-Ray Images
    标题:用于冠状病毒胸片图像稳健分类的双增强结构
    作者:Kartikeya Badola,Sameer Ambekar,Himanshu Pant,Sumit Soman,Anuradha Sural,Rajiv Narang,Suresh Chandra,Jayadeva
    机构:Indian Institute of Technology Delhi, India, Max Hospital, Vaishali, India, All India Institute of Medical Sciences, New Delhi, India, r_narangdyahoo. com, jayadevagee. iitd. ac.in, February
    链接:https://arxiv.org/abs/2102.07975
    【115】 Follow-the-Regularizer-Leader Routes to Chaos in Routing Games
    标题:跟随者-规则者-领导者在路径博弈中走向混乱
    作者:Jakub Bielawski,Thiparat Chotibut,Fryderyk Falniowski,Grzegorz Kosiorowski,Michał Misiurewicz,Georgios Piliouras
    备注:30 pages, 8 figures
    链接:https://arxiv.org/abs/2102.07974
    【116】 Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach
    标题:无线网络上的联合学习:一种带宽受限的协调下降方法
    作者:Junshan Zhang,Na Li,Mehmet Dedeoglu
    机构: Computer and Energy Engineering, Arizona State University, Harvard University
    链接:https://arxiv.org/abs/2102.07972
    【117】 Machine Learning Based Cyber Attacks Targeting on Controlled Information: A Survey
    标题:基于机器学习的针对受控信息的网络攻击研究综述
    作者:Yuantian Miao,Chao Chen,Lei Pan,Qing-Long Han,Jun Zhang,Yang Xiang
    备注:Under 3rd round review of ACM Computing Surveys
    链接:https://arxiv.org/abs/2102.07969
    【118】 Efficient Discretizations of Optimal Transport
    标题:最优运输的有效离散化
    作者:Junqi Wang,Pei Wang,Patrick Shafto
    备注:17 pages, 14 figures. Submitted to ICML 2021
    链接:https://arxiv.org/abs/2102.07956
    【119】 DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
    标题:DFAC框架:基于分位数混合分解值函数的多Agent分布式Q-Learning
    作者:Wei-Fang Sun,Cheng-Kuang Lee,Chun-Yi Lee
    链接:https://arxiv.org/abs/2102.07936
    【120】 Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation
    标题:基于分层转换器的大上下文端到端ASR与大上下文知识提取
    作者:Ryo Masumura,Naoki Makishima,Mana Ihori,Akihiko Takashima,Tomohiro Tanaka,Shota Orihashi
    机构:NTT Media Intelligence Laboratories, NTT Corporation, Japan
    备注:Accepted at ICASSP 2021
    链接:https://arxiv.org/abs/2102.07935
    【121】 GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software
    标题:GraphGallery:基于图神经网络的智能软件快速标杆和简易开发平台
    作者:Jintang Li,Kun Xu,Liang Chen,Zibin Zheng,Xiao Liu
    机构:Sun Yat-sen University, China, Deakin University, australia
    备注:4 pages Demonstrations track paper accepted at ICSE 2021
    链接:https://arxiv.org/abs/2102.07933
    【122】 A Hidden Challenge of Link Prediction: Which Pairs to Check?
    标题:链接预测的一个隐藏挑战:应该检查哪些对?
    作者:Caleb Belth,Alican Büyükçakır,Danai Koutra
    机构:Alican Buyukcakir, University of Michigan, University of michigan, Ann Arbor, MI, USA
    链接:https://arxiv.org/abs/2102.07878
    【123】 Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
    标题:基于熵正则最优传输的可微粒子滤波
    作者:Adrien Corenflos,James Thornton,Arnaud Doucet,George Deligiannidis
    链接:https://arxiv.org/abs/2102.07850
    【124】 Self-Supervised Features Improve Open-World Learning
    标题:自我监督功能促进开放世界学习
    作者:Akshay Raj Dhamija,Touqeer Ahmad,Jonathan Schwan,Mohsen Jafarzadeh,Chunchun Li,Terrance E. Boult
    机构:Vision and Security Technology Lab, University of Colorado at Colorado Springs, Colorado Springs
    链接:https://arxiv.org/abs/2102.07848
    【125】 Meta Back-translation
    标题:元回译
    作者:Hieu Pham,Xinyi Wang,Yiming Yang,Graham Neubig
    机构:Equal contributions, Language Technology Institute, Carnegie Mellon University, Pittsburgh, PA , Google Al, Brain Team, Mountain View, CA
    备注:Accepted to ICLR 2021
    链接:https://arxiv.org/abs/2102.07847
    【126】 Controlling False Discovery Rates Using Null Bootstrapping
    标题:使用Null Bootstrapping控制错误发现率
    作者:Junpei Komiyama,Masaya Abe,Kei Nakagawa,Kenichiro McAlinn
    链接:https://arxiv.org/abs/2102.07826
    【127】 Anomalous Sound Detection with Machine Learning: A Systematic Review
    标题:机器学习在异常声音检测中的系统评价
    作者:Eduardo C. Nunes
    链接:https://arxiv.org/abs/2102.07820
    【128】 Enhancing the Spatio-temporal Observability of Grid-Edge Resources in Distribution Grids
    标题:提高配电网边缘资源的时空可观测性
    作者:Shanny Lin,Hao Zhu
    备注:Submitted to IEEE Transactions on Smart Grid, 8 pages, 8 figures
    链接:https://arxiv.org/abs/2102.07801
    【129】 Top-$k$ eXtreme Contextual Bandits with Arm Hierarchy
    标题:Top-$k$Extreme Context Bandits with ARM Hierarchy
    作者:Rajat Sen,Alexander Rakhlin,Lexing Ying,Rahul Kidambi,Dean Foster,Daniel Hill,Inderjit Dhillon
    机构:February
    链接:https://arxiv.org/abs/2102.07800
    【130】 Universal Adversarial Examples and Perturbations for Quantum Classifiers
    标题:量子分类器的通用对抗性例子和扰动
    作者:Weiyuan Gong,Dong-Ling Deng
    机构:Center for Quantum Information, IIIS, Tsinghua University Beijing , People’s Republic of China, Shanghai Qi Zhi Institute,th Floor, AI Tower, No. , Yunjin Road, Xuhui District, Shanghai , China
    链接:https://arxiv.org/abs/2102.07788
    【131】 PeriodNet: A non-autoregressive waveform generation model with a structure separating periodic and aperiodic components
    标题:周期网:一种周期分量与非周期分量分离的非自回归波形生成模型
    作者:Yukiya Hono,Shinji Takaki,Kei Hashimoto,Keiichiro Oura,Yoshihiko Nankaku,Keiichi Tokuda
    机构:Nagoya Institute of Technology, Nagoya, Japan
    备注:5 pages, accepted to ICASSP 2021
    链接:https://arxiv.org/abs/2102.07786
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