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历史文章列表 网站https://www.arxivdaily.com/
注：含中英文摘要速递见公众号【arXiv每日学术速递】,涵盖CS|物理|数学|经济|统计|金融|生物|电气等领域。 cs.AI人工智能，共计60篇
【1】 A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
标题：强化学习中样本有效函数逼近的通用框架
链接：https://arxiv.org/abs/2209.15634
作者：Zixiang Chen,Chris Junchi Li,Angela Yuan,Quanquan Gu,Michael I. Jordan
机构：Department of Computer Sciences, University of California, Los Angeles‡, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley⋄, Department of Statistics, University of California, Berkeley†
【2】 Beyond Bayes-optimality: meta-learning what you know you don't know
标题：超越贝叶斯-最优性：元-学习你知道你不知道的东西
链接：https://arxiv.org/abs/2209.15618
作者：Jordi Grau-Moya,Grégoire Delétang,Markus Kunesch,Tim Genewein,Elliot Catt,Kevin Li,Anian Ruoss,Chris Cundy,Joel Veness,Jane Wang,Marcus Hutter,Christopher Summerfield,Shane Legg,Pedro Ortega
机构：Equal contribution,DeepMind, London,Department of Computer Science University of California, Berkeley
备注：33 pages, 8 figures, technical report
【3】 MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction
标题：MEIM：超越分块项格式的多分区嵌入交互，用于高效和富有表现力的链接预测
链接：https://arxiv.org/abs/2209.15597
作者：Hung-Nghiep Tran,Atsuhiro Takasu
机构：National Institute of Informatics, Japan, The Graduate University for Advanced Studies, SOKENDAI, Japan
备注：Accepted at the International Joint Conference on Artificial Intelligence (IJCAI), 2022; add appendix with extra experiments
【4】 Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks
标题：联合学习中数据异构性的再思考：引入新概念和标准基准
链接：https://arxiv.org/abs/2209.15595
作者：Mahdi Morafah,Saeed Vahidian,Chen Chen,Mubarak Shah,Bill Lin
机构：UC San Diego, UCF
备注：arXiv admin note: text overlap with arXiv:2209.10526
【5】 Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions
标题：通过从问题描述中自动生成优化模型来增强运筹学研究
链接：https://arxiv.org/abs/2209.15565
作者：Rindranirina Ramamonjison,Haley Li,Timothy T. Yu,Shiqi He,Vishnu Rengan,Amin Banitalebi-Dehkordi,Zirui Zhou,Yong Zhang
机构： University of British Columbiaavoids the manual writing of the formulation by usinga modeling language
备注：6 pages text, 23 pages supplementary material
【6】 Using Knowledge Distillation to improve interpretable models in a retail banking context
标题：使用知识精馏改进零售银行环境中的可解释模型
链接：https://arxiv.org/abs/2209.15496
作者：Maxime Biehler,Mohamed Guermazi,Célim Starck
机构：BPCE SA, Regulatory AI Department
备注：25 pages, 9 figures, 11 tables
【7】 Towards General-Purpose Representation Learning of Polygonal Geometries
标题：面向多边形几何的通用表示学习
链接：https://arxiv.org/abs/2209.15458
作者：Gengchen Mai,Chiyu Jiang,Weiwei Sun,Rui Zhu,Yao Xuan,Ling Cai,Krzysztof Janowicz,Stefano Ermon,Ni Lao
机构：Spatially Explicit Artificial Intelligence Lab, Department of, Geography, University of Georgia, Athens, Georgia, USA., Department of Computer Science, Stanford University, Stanford, California, USA., STKO Lab, University of California Santa Barbara, Santa
备注：58 pages, 20 figures, Accepted to GeoInformatica
【8】 Scheduling for Urban Air Mobility using Safe Learning
标题：基于安全学习的城市空中机动性调度
链接：https://arxiv.org/abs/2209.15457
作者：Surya Murthy,Natasha A. Neogi,Suda Bharadwaj
机构：School of Computer Engineering, University of Illinois, Urbana-Champaign, Urbana, Illinois, NASA Langley Research Center, Hampton, Virginia, Skygrid LLC., Austin, Texas
备注：None
【9】 GPNet: Simplifying Graph Neural Networks via Multi-channel Geometric Polynomials
标题：GPNet：利用多通道几何多项式简化图神经网络
链接：https://arxiv.org/abs/2209.15454
作者：Xun Liu,Alex Hay-Man Ng,Fangyuan Lei,Yikuan Zhang,Zhengmin Li
机构：School of Information Engineering, Guangdong University of Technology, Department of Electronics, Software Engineering Institute of Guangzhou, School of Civil and Transportation Engineering, Guangdong University of Technology
备注：15 pages, 15 figures
【10】 Safe Exploration Method for Reinforcement Learning under Existence of Disturbance
标题：扰动存在下强化学习的安全探索方法
链接：https://arxiv.org/abs/2209.15452
作者：Yoshihiro Okawa,Tomotake Sasaki,Hitoshi Yanami,Toru Namerikawa
机构： Artificial Intelligence Laboratory, Fujitsu Limited, Kawasaki, Japan, Department of System Design Engineering, Keio University, Yokohama, Japan
备注：Accepted to the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) 2022
【11】 Relative representations enable zero-shot latent space communication
标题：相对表示使Zero-Shot潜在空间通信成为可能
链接：https://arxiv.org/abs/2209.15430
作者：Luca Moschella,Valentino Maiorca,Marco Fumero,Antonio Norelli,Francesco Locatello,Emanuele Rodolà
机构：Emanuele Rodola, Sapienza University of Rome, Amazon Web Services
备注：20 pages, 8 figures, 16 tables
【12】 Tuning of Mixture-of-Experts Mixed-Precision Neural Networks
标题：混合专家混合精度神经网络的整定
链接：https://arxiv.org/abs/2209.15427
作者：Fabian Tschopp
机构：Supervisor: Dr. Matthew Cook, Institute of Neuroinformatics , UZH & ETHZ, arXiv:,.,v, [cs.LG] , Sep
备注：55 pages
【13】 Parea: multi-view ensemble clustering for cancer subtype discovery
标题：PAREA：用于癌症亚型发现的多视点集成聚类
链接：https://arxiv.org/abs/2209.15399
作者：Bastian Pfeifer,Marcus D. Bloice,Michael G. Schimek
机构：Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Austria
【14】 Evaluation of importance estimators in deep learning classifiers for Computed Tomography
标题：计算机层析成像深度学习分类器中重要性估计器的评估
链接：https://arxiv.org/abs/2209.15398
作者：Lennart Brocki,Wistan Marchadour,Jonas Maison,Bogdan Badic,Panagiotis Papadimitroulas,Mathieu Hatt,Franck Vermet,Neo Christopher Chung
机构：Christopher Chung b,∗, Institute of Informatics, University of Warsaw, Warsaw, Poland, LaTIM, INSERM, UMR , Univ Brest, Brest, France, LMBA, CNRS, UMR , Univ Brest, Brest, France, Aquilab, Lille, France
备注：None
【15】 Programmable Control of Ultrasound Swarmbots through Reinforcement Learning
标题：基于强化学习的超声波群机器人可编程控制
链接：https://arxiv.org/abs/2209.15393
作者：Matthijs Schrage,Mahmoud Medany,Daniel Ahmed
机构：M. Scharge, M. Medany, D. Ahmed, Acoustic Robotics Systems Lab, Institute of Robotics and Intelligent Systems, Department of Mechanical and Process Engineering, ETH Zurich , Switzerland
【16】 Automatic Context-Driven Inference of Engagement in HMI: A Survey
标题：人机界面中参与的语境驱动自动推理研究综述
链接：https://arxiv.org/abs/2209.15370
作者：Hanan Salam,Oya Celiktutan,Hatice Gunes,Mohamed Chetouani
机构： Gunes is with the Department of Computer Science and Technol-ogy, University of Cambridge
【17】 SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data
标题：SpeechLM：使用不成对的文本数据进行增强的语音预训练
链接：https://arxiv.org/abs/2209.15329
作者：Ziqiang Zhang,Sanyuan Chen,Long Zhou,Yu Wu,Shuo Ren,Shujie Liu,Zhuoyuan Yao,Xun Gong,Lirong Dai,Jinyu Li,Furu Wei
机构：University of Science and Technology of China, Harbin Institute of Technology, Microsoft
备注：14 pages
【18】 Observational Robustness and Invariances in Reinforcement Learning via Lexicographic Objectives
标题：基于词典目标的强化学习中的观测稳健性和不变性
链接：https://arxiv.org/abs/2209.15320
作者：Daniel Jarne Ornia,Licio Romao,Lewis Hammond,Manuel Mazo Jr.,Alessandro Abate
机构：Delft University of Technology, The Netherlands, University of Oxford, United Kingdom
【19】 Convolutional Neural Networks Quantization with Attention
标题：带注意力的卷积神经网络量化
链接：https://arxiv.org/abs/2209.15317
作者：Binyi Wu,Bernd Waschneck,Christian Georg Mayr
机构：Dresden, Germany, www.tu-dresden.de, Division of Power and Sensor Systems, Infineon Technologies AG, K¨onigsbr¨ucker Str. , Centre for Tactile Internet with Human-in-the-loop (CeTI), Technische Universit¨at Dresden, Helmholtzstr.
备注：Preprint of an article published in International Journal of Neural Systems, [10.1142/S0129065722500514] \c{opyright} [copyright World Scientific Publishing Company] [this https URL]
【20】 Effective Early Stopping of Point Cloud Neural Networks
标题：点云神经网络的有效早期停止
链接：https://arxiv.org/abs/2209.15308
作者：Thanasis Zoumpekas,Maria Salamó,Anna Puig
机构： WAI Research Group, Department of Mathematics and Computer Science, UBICS Institute, University of Barcelona, Barcelona, Spain, IMUB Institute, University of Barcelona, Barcelona, Spain
【21】 Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks
标题：基于量化自关注深度神经网络的可验证、高能效医学图像分析
链接：https://arxiv.org/abs/2209.15287
作者：Rakshith Sathish,Swanand Khare,Debdoot Sheet
机构：Indian Institute of Technology Kharagpur, West Bengal, India
备注：Accepted at MICCAI 2022 FAIR Workshop
【22】 Learning Transferable Spatiotemporal Representations from Natural Script Knowledge
标题：从自然文字知识中学习可迁移的时空表示
链接：https://arxiv.org/abs/2209.15280
作者：Ziyun Zeng,Yuying Ge,Xihui Liu,Bin Chen,Ping Luo,Shu-Tao Xia,Yixiao Ge
机构：∗equal contribution, †corresponding authors, Tsinghua University, Tencent PCG, The University of Hong Kong, Harbin Institute of Technology, Shenzhen
【23】 Rethinking skip connection model as a learnable Markov chain
标题：对跳跃连接模型作为可学习马尔可夫链的再思考
链接：https://arxiv.org/abs/2209.15278
作者：Dengsheng Chen,Jie Hu,Wenwen Qiang,Xiaoming Wei,Enhua Wu
机构：Meituan Inc., State Key Laboratory of Computer Science, ISCAS, University of Chinese Academy of Sciences, Institute of Software Chinese Academy of Sciences, University of Macau
备注：12 pages, 4 figures
【24】 Machine Unlearning Method Based On Projection Residual
标题：基于投影残差的机器遗忘方法
链接：https://arxiv.org/abs/2209.15276
作者：Zihao Cao,Jianzong Wang,Shijing Si,Zhangcheng Huang,Jing Xiao
机构：Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China, School of Economics and Finance, Shanghai International Studies University, Shanghai, China
备注：This paper is accepted by DSAA2022. The 9th IEEE International Conference on Data Science and Advanced Analytics
【25】 A Multivariate Complexity Analysis of Qualitative Reasoning Problems
标题：定性推理问题的多元复杂性分析
链接：https://arxiv.org/abs/2209.15275
作者：Leif Eriksson,Victor Lagerkvist
【26】 Online Multi-Agent Decentralized Byzantine-robust Gradient Estimation
标题：在线多智能体分散拜占庭稳健梯度估计
链接：https://arxiv.org/abs/2209.15274
作者：Alexandre Reiffers-Masson,Isabel Amigo
【27】 Application-Driven AI Paradigm for Human Action Recognition
标题：应用驱动的人工智能人类行为识别范式
链接：https://arxiv.org/abs/2209.15271
作者：Zezhou Chen,Yajie Cui,Kaikai Zhao,Zhaoxiang Liu,Shiguo Lian
机构：ChinaUnicom
【28】 Diffusion-based Image Translation using Disentangled Style and Content Representation
标题：基于扩散的解缠风格和内容表示的图像翻译
链接：https://arxiv.org/abs/2209.15264
作者：Gihyun Kwon,Jong Chul Ye
机构：Department of Bio and Brain Engineering, Kim Jaechul Graduate School of AI, KAIST
【29】 A Multiple Criteria Decision Analysis based Approach to Remove Uncertainty in SMP Models
标题：一种基于多准则决策分析的SMP模型不确定性消除方法
链接：https://arxiv.org/abs/2209.15260
作者：Gokul Yenduri,Thippa Reddy Gadekallu
机构：School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu , India.
备注：Submitted for peer review
【30】 SoK: On the Impossible Security of Very Large Foundation Models
标题：SOK：超大基础模型的不可能安全性
链接：https://arxiv.org/abs/2209.15259
作者：El-Mahdi El-Mhamdi,Sadegh Farhadkhani,Rachid Guerraoui,Nirupam Gupta,Lê-Nguyên Hoang,Rafael Pinot,John Stephan
机构：´Ecole Polytechnique, Palaiseau, France, IC, EPFL, Lausanne, Switzerland, Lˆe Nguyˆen Hoang, Association Tournesol
备注：13 pages
【31】 Prompt Tuning for Graph Neural Networks
标题：图神经网络的快速整定
链接：https://arxiv.org/abs/2209.15240
作者：Taoran Fang,Yunchao Zhang,Yang Yang,Chunping Wang
机构：Zhejiang University, FinVolution Group
【32】 GM-VAE: Representation Learning with VAE on Gaussian Manifold
标题：GM-VAE：基于高斯流形上的VAE表示学习
链接：https://arxiv.org/abs/2209.15217
作者：Seunghyuk Cho,Juyong Lee,Dongwoo Kim
机构：Pohang University of Science and Technology
备注：17 pages, 7 figures
【33】 Construction and Applications of Open Business Knowledge Graph
标题：开放式商务知识图谱的构建与应用
链接：https://arxiv.org/abs/2209.15214
作者：Shumin Deng,Hui Chen,Zhoubo Li,Feiyu Xiong,Qiang Chen,Mosha Chen,Xiangwen Liu,Jiaoyan Chen,Jeff Z. Pan,Huajun Chen,Ningyu Zhang
机构： Zhejiang University & AZFT Joint Lab for Knowledge Engine, China, Hangzhou Innovation Center, Zhejiang University, China, Alibaba Group, China, University of Oxford, United Kingdom, University of Edinburgh, United Kingdom
备注：Work in Progress
【34】 Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel
标题：具有连通正切核的比例不变贝叶斯神经网络
链接：https://arxiv.org/abs/2209.15208
作者：SungYub Kim,Sihwan Park,Kyungsu Kim,Eunho Yang
机构：Korea Advanced Institute of Science and Technology (KAIST), Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea
【35】 What Makes Pre-trained Language Models Better Zero/Few-shot Learners?
标题：是什么让预先训练的语言模型更好地成为零/少机会学习者？
链接：https://arxiv.org/abs/2209.15206
作者：Jinghui Lu,Rui Zhao,Brian Mac Namee,Dongsheng Zhu,Weidong Han,Fei Tan
机构：SenseTime Research, School of Computer Science, University College Dublin, The Insight Centre for Data Analytics, University College Dublin, Fudan University
【36】 ASPiRe:Adaptive Skill Priors for Reinforcement Learning
标题：ASPIRE：强化学习的自适应技能先验
链接：https://arxiv.org/abs/2209.15205
作者：Mengda Xu,Manuela Veloso,Shuran Song
机构： Department of Computer Science, Columbia University, J.P. Morgan AI Research , School of Computer Science, Carnegie Mellon University (emeritus)
备注：36th Conference on Neural Information Processing Systems (NeurIPS 2022)
【37】 Synonym Detection Using Syntactic Dependency And Neural Embeddings
标题：基于句法依赖和神经嵌入的同义词检测
链接：https://arxiv.org/abs/2209.15202
作者：Dongqiang Yang,Pikun Wang,Xiaodong Sun,Ning Li
机构：School of Computer Science and Technology, Shandong Jianzhu University, Jinan , China
备注：None
【38】 Evaluation of taxonomic and neural embedding methods for calculating semantic similarity
标题：语义相似度计算的分类法和神经嵌入法的评价
链接：https://arxiv.org/abs/2209.15197
作者：Dongqiang Yang,Yanqin Yin
机构：School of Computer Science and Technology, Shandong Jianzhu University, China
备注：None
【39】 Learning by Distilling Context
标题：在提炼语境中学习
链接：https://arxiv.org/abs/2209.15189
作者：Charlie Snell,Dan Klein,Ruiqi Zhong
机构：University of California, Berkeley, EECS Department
【40】 RL-MD: A Novel Reinforcement Learning Approach for DNA Motif Discovery
标题：RL-MD：一种新的DNA基序发现强化学习方法
链接：https://arxiv.org/abs/2209.15181
作者：Wen Wang,Jianzong Wang,Shijing Si,Zhangcheng Huang,Jing Xiao
机构：Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China, School of Economics and Finance, Shanghai International Studies University, Shanghai, China
备注：This paper is accepted by DSAA2022. The 9th IEEE International Conference on Data Science and Advanced Analytics
【41】 Adaptive Sparse and Monotonic Attention for Transformer-based Automatic Speech Recognition
标题：基于Transformer的自动语音识别的自适应稀疏单调注意
链接：https://arxiv.org/abs/2209.15176
作者：Chendong Zhao,Jianzong Wang,Wen qi Wei,Xiaoyang Qu,Haoqian Wang,Jing Xiao
机构：† Ping An Technology (Shenzhen) Co., Ltd., Shenzhen, China, ⋆ The Shenzhen International Graduate School, Tsinghua University, China
备注：Accepted to DSAA 2022
【42】 Reward Shaping for User Satisfaction in a REINFORCE Recommender
标题：强化推荐器中用户满意度的奖励塑造
链接：https://arxiv.org/abs/2209.15166
作者：Konstantina Christakopoulou,Can Xu,Sai Zhang,Sriraj Badam,Trevor Potter,Daniel Li,Hao Wan,Xinyang Yi,Ya Le,Chris Berg,Eric Bencomo Dixon,Ed H. Chi,Minmin Chen
备注：Accepted in Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 38th International Conference on Machine Learning, 2021
【43】 Blur the Linguistic Boundary: Interpreting Chinese Buddhist Sutra in English via Neural Machine Translation
标题：模糊语言边界--用神经机器翻译实现汉语佛经英译
链接：https://arxiv.org/abs/2209.15164
作者：Denghao Li,Yuqiao Zeng,Jianzong Wang,Lingwei Kong,Zhangcheng Huang,Ning Cheng,Xiaoyang Qu,Jing Xiao
机构：♯Ping An Technology (Shenzhen) Co., Ltd., China, ⋄University of Electronic Science and Technology of China, China
备注：This paper is accepted by ICTAI 2022. The 34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
【44】 MobileViTv3: Mobile-Friendly Vision Transformer with Simple and Effective Fusion of Local, Global and Input Features
标题：MobileViTv3：简单有效地融合局部、全局和输入特征的移动友好视觉转换器
链接：https://arxiv.org/abs/2209.15159
作者：Shakti N. Wadekar,Abhishek Chaurasia
备注：20 pages, 7 figures
【45】 Rethinking and Recomputing the Value of ML Models
标题：对最大似然模型价值的重新思考和重新计算
链接：https://arxiv.org/abs/2209.15157
作者：Burcu Sayin,Fabio Casati,Andrea Passerini,Jie Yang,Xinyue Chen
机构： University of Trento Via Sommarive , Povo, Trento, Italy, Servicenow Santa Clara, CA, USA, Delft University of Technology Mekelweg , CD Delft, Netherlands
【46】 Ensemble Machine Learning Model Trained on a New Synthesized Dataset Generalizes Well for Stress Prediction Using Wearable Devices
标题：在新的合成数据集上训练的集成机器学习模型对于使用可穿戴设备进行应力预测具有很好的泛化能力
链接：https://arxiv.org/abs/2209.15146
作者：Gideon Vos,Kelly Trinh,Zoltan Sarnyai,Mostafa Rahimi Azghadi
机构：• We show that existing research using machine learning techniques have, utilized small datasets that fail to generalize on new, unseen stress, biomarker data., • We propose a synthesizing method and produce a new dataset by en-
备注：37 pages, 11 figures
【47】 Machine Learning for Stress Monitoring from Wearable Devices: A Systematic Literature Review
标题：机器学习在可穿戴设备应力监测中的应用
链接：https://arxiv.org/abs/2209.15137
作者：Gideon Vos,Kelly Trinh,Zoltan Sarnyai,Mostafa Rahimi Azghadi
机构：arXiv:,.,v, [cs.AI] , Sep
备注：50 pages, 8 figures
【48】 Modeling driver's evasive behavior during safety-critical lane changes:Two-dimensional time-to-collision and deep reinforcement learning
标题：基于二维碰撞时间和深度强化学习的驾驶员安全换道避让行为建模
链接：https://arxiv.org/abs/2209.15133
作者：Hongyu Guo,Kun Xie,Mehdi Keyvan-Ekbatani
机构：Complex Transport Systems Laboratory (CTSLAB), Department of Civil and Natural Resources Engineering, University of, Canterbury, Private Bag , Christchurch , New Zealand
【49】 A Quantitative Account of Harm
标题：对危害的量化描述
链接：https://arxiv.org/abs/2209.15111
作者：Sander Beckers,Hana Chockler,Joseph Y. Halpern
机构：Cluster of Excellence in Machine Learning, University of T¨ubingen, causaLens and, Department of Informatics, King’s College London, Computer Science Department, Cornell University
备注：17 pages, under submissions
【50】 How to tackle an emerging topic? Combining strong and weak labels for Covid news NER
标题：如何应对一个新出现的话题？Covid News Ner强弱结合的标签
链接：https://arxiv.org/abs/2209.15108
作者：Aleksander Ficek,Fangyu Liu,Nigel Collier
机构：University of Waterloo, University of Cambridge
备注：AACL 2022
【51】 OAK4XAI: Model towards Out-Of-Box eXplainable Artificial Intelligence for Digital Agriculture
标题：OAK4XAI：面向数字农业的开箱即用可解释人工智能模型
链接：https://arxiv.org/abs/2209.15104
作者：Quoc Hung Ngo,Tahar Kechadi,Nhien-An Le-Khac
机构：School of Computer Science, College of Science, University College Dublin, Belfield, Dublin , Ireland
备注：AI-2022 Forty-second SGAI International Conference on Artificial Intelligence
【52】 Zero-shot visual reasoning through probabilistic analogical mapping
标题：基于概率类比映射的Zero-Shot视觉推理
链接：https://arxiv.org/abs/2209.15087
作者：Taylor W. Webb,Shuhao Fu,Trevor Bihl,Keith J. Holyoak,Hongjing Lu
机构：Equal contribution, Department of Psychology, University of California, Los Angeles, Air Force Research Laboratory, Department of Statistics, University of California, Los Angeles
【53】 Few-shot Text Classification with Dual Contrastive Consistency
标题：具有双重对比一致性的少射文本分类
链接：https://arxiv.org/abs/2209.15069
作者：Liwen Sun,Jiawei Han
机构：University of Illinois at Urbana-Champaign, IL, USA
备注：8 pages, 2 figures, under review
【54】 Reasoning about Complex Networks: A Logic Programming Approach
标题：复杂网络推理：一种逻辑规划方法
链接：https://arxiv.org/abs/2209.15067
作者：Paulo Shakarian,Gerardo I. Simari,Devon Callahan
机构： Network Science Center and Dept. of Electrical Engineering and Computer Science, U.S. Military Academy, West Point, NY , USA, Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford OX,QD, United Kingdom
备注：arXiv admin note: substantial text overlap with arXiv:1301.0302
【55】 Graph Attention Network for Camera Relocalization on Dynamic Scenes
标题：动态场景下摄像机重定位的图注意网络
链接：https://arxiv.org/abs/2209.15056
作者：Mohamed Amine Ouali,Mohamed Bouguessa,Riadh Ksantini
机构：Department of Computer Science, University of Quebec at Montreal, Montreal, QC, Canada, University of Bahrain, Zallaq, Bahrain
【56】 Generalizability of Adversarial Robustness Under Distribution Shifts
标题：分布漂移下对抗性稳健性的泛化
链接：https://arxiv.org/abs/2209.15042
作者：Kumail Alhamoud,Hasan Abed Al Kader Hammoud,Motasem Alfarra,Bernard Ghanem
机构：KAUST, Saudi Arabia
【57】 Guided Unsupervised Learning by Subaperture Decomposition for Ocean SAR Image Retrieval
标题：用于海洋SAR图像检索的子孔径分解无监督导引学习
链接：https://arxiv.org/abs/2209.15034
作者：Nicolae-Cătălin Ristea,Andrei Anghel,Mihai Datcu,Bertrand Chapron
机构： Anghel are with the Research Centre for SpatialInformation (CEOSpaceTech) and the Department of Telecommunications, University Politehnica of Bucharest, UniversityPolitehnicaofBucharest, Romaniaandthe Remote Sensing Technology Institute
【58】 Protein structure generation via folding diffusion
标题：折叠扩散法生成蛋白质结构
链接：https://arxiv.org/abs/2209.15611
作者：Kevin E. Wu,Kevin K. Yang,Rianne van den Berg,James Y. Zou,Alex X. Lu,Ava P. Amini
机构：Stanford University, Microsoft Research
【59】 Accurate Long-term Air Temperature Prediction with a Fusion of Artificial Intelligence and Data Reduction Techniques
标题：融合人工智能和数据约简技术的精确长期气温预报
链接：https://arxiv.org/abs/2209.15424
作者：Dušan Fister,Jorge Pérez-Aracil,César Peláez-Rodríguez,Javier Del Ser,Sancho Salcedo-Sanz
机构：Department of Signal Processing and Communications. University of Alcala, Madrid, Spain., Department of Computer Systems Engineering, Universidad Polit´ecnica de Madrid, Madrid, TECNALIA, Basque Research & Technology Alliance (BRTA), Derio, Spain
备注：33 pages, 14 figures, 7 tables, under review
【60】 Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions
标题：大深度网的隐偏性：一种非线性函数的秩概念
链接：https://arxiv.org/abs/2209.15055
作者：Arthur Jacot
机构：Courant Institute of Mathematical Sciences, New York University, New York, NY , USA
机器翻译由腾讯交互翻译提供，仅供参考
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发表于 2022-11-26 15:49:44