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44篇

【1】 Large Language Models with Controllable Working Memory
标题：具有可控工作记忆的大型语言模型
链接：https://arxiv.org/abs/2211.05110
作者：Daliang Li,Ankit Singh Rawat,Manzil Zaheer,Xin Wang,Michal Lukasik,Andreas Veit,Felix Yu,Sanjiv Kumar
机构：Google Research Deepmind

【2】 Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models
标题：安全潜在扩散：缓解扩散模型中的不适当退化
链接：https://arxiv.org/abs/2211.05105
作者：Patrick Schramowski,Manuel Brack,Bjrn Deiseroth,Kristian Kersting
机构：Bj¨orn Deiseroth, Computer Science Department, TU Darmstadt,Centre for Cognitive Science, TU Darmstadt, Hessian Center for AI (hessian.AI),German Center for Artificial Intelligence (DFKI), Aleph Alpha,LAION

【3】 Cross-lingual Transfer Learning for Check-worthy Claim Identification over Twitter
标题：基于Twitter的可核查索赔识别的跨语言迁移学习
链接：https://arxiv.org/abs/2211.05087
作者：Maram Hasanain,Tamer Elsayed
机构：Qatar University, Doha, Qatar

【4】 What is Wrong with Language Models that Can Not Tell a Story?
标题：不会讲故事的语言模型有什么问题？
链接：https://arxiv.org/abs/2211.05044
作者：Ivan P. Yamshchikov,Alexey Tikhonov
机构：Max Planck Institute for, Mathematics in the Sciences, Leipzig, Germany, CEMAPRE, University of Lisbon, Portugal, Inworld.AI, Berlin, Germany

【5】 Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task
标题：多模式时态数据的主动获取：一项具有挑战性的决策任务
链接：https://arxiv.org/abs/2211.05039
作者：Jannik Kossen,Ctlina CangeaEszter Vértes,Andrew Jaegle,Viorica Patraucean,Ira Ktena,Nenad Tomasev,Danielle Belgrave
机构：Catalina Cangea,, Eszter Vertes, OATML, Department of Computer Science, University of Oxford, DeepMind
备注：Foundation Models for Decision Making Workshop at Neural Information Processing Systems 2022

【6】 The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks
标题：两全其美：一个将退化预测与高性能超分辨率网络相结合的框架
链接：https://arxiv.org/abs/2211.05018
作者：Matthew Aquilina,Keith George Ciantar,Christian Galea,Kenneth P. Camilleri,Reuben A. Farrugia,John Abela
机构：Department of Communications & Computer Engineering, University of Malta, Msida, Malta;, Deanery of Molecular, Genetic & Population Health Sciences, University of Edinburgh, Edinburgh, Scotland, UK;

【7】 RL-DWA Omnidirectional Motion Planning for Person Following in Domestic Assistance and Monitoring
标题：RL-DWA在家政救助和监测中的人员跟随全方位运动规划
链接：https://arxiv.org/abs/2211.04993
作者：Andrea Eirale,Mauro Martini,Marcello Chiaberge
机构：Department of Electronics, and Telecommunications (DET), Politecnico di Torino, Torino, Italy

【8】 Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information
标题：具有实时信息的绿色安全博弈的可解释深度强化学习
链接：https://arxiv.org/abs/2211.04987
作者：Vishnu Dutt Sharma,John P. Dickerson,Pratap Tokekar
机构：University of Maryland, College Park MD , USA

【9】 Workload Forecasting of a Logistic Node Using Bayesian Neural Networks
标题：基于贝叶斯神经网络的物流节点负荷预测
链接：https://arxiv.org/abs/2211.04976
作者：Emin Nakilcioglu,Anisa Rizvanolli und Olaf Rendel
备注：None

【10】 Leveraging Offline Data in Online Reinforcement Learning
标题：在在线强化学习中利用离线数据
链接：https://arxiv.org/abs/2211.04974
作者：Andrew Wagenmaker,Aldo Pacchiano

【11】 Graph Neural Networks with Adaptive Readouts
标题：具有自适应读数的图神经网络
链接：https://arxiv.org/abs/2211.04952
作者：David Buterez,Jon Paul Janet,Steven J. Kiddle,Dino Oglic,Pietro Liò
机构： Department of Computer Science and Technology, University of Cambridge, UK, CVRM, BioPharmaceuticals R&D, AstraZeneca, Sweden, DS&AI, BioPharmaceuticals R&D, AstraZeneca, UK
备注：Published at NeurIPS 2022. 10 pages, 5 figures, 1 table

【12】 Utilising Bayesian Networks to combine multimodal data and expert opinion for the robust prediction of depression and its symptoms
标题：利用贝叶斯网络结合多模式数据和专家意见对抑郁症及其症状进行稳健预测
链接：https://arxiv.org/abs/2211.04924
作者：Salvatore Fara,Orlaith Hickey,Alexandra Georgescu,Stefano Goria,Emilia Molimpakis,Nicholas Cummins
机构：Thymia, London, UK, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King’s College London, London, UK
备注：Submitted to ICASSP 2023

【13】 Mask More and Mask Later: Efficient Pre-training of Masked Language Models by Disentangling the [MASK] Token
标题：屏蔽更多，屏蔽更晚：通过解开[屏蔽]令牌来高效地预训练被屏蔽的语言模型
链接：https://arxiv.org/abs/2211.04898
作者：Baohao Liao,David Thulke,Sanjika Hewavitharana,Hermann Ney,Christof Monz
机构：University of Amsterdam, RWTH Aachen University, eBay
备注：Code available at: this https URL

【14】 Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patterns
标题：基于时态逻辑模式的面向结果的规范过程监控
链接：https://arxiv.org/abs/2211.04880
作者：Ivan Donadello,Chiara Di Francescomarino,Fabrizio Maria Maggi,Francesco Ricci,Aladdin Shikhizada
机构：Free University of Bozen-Bolzano, University of Trento, Visioncraft O¨U
备注：38 pages, 6 figures, 8 tables

【15】 Foundation Models for Semantic Novelty in Reinforcement Learning
标题：强化学习中语义新颖性的基础模型
链接：https://arxiv.org/abs/2211.04878
作者：Tarun Gupta,Peter Karkus,Tong Che,Danfei Xu,Marco Pavone
机构：NVIDIA Research, University of Oxford, Georgia Institute of Technology, Stanford University
备注：Foundation Models for Decision Making Workshop at Neural Information Processing Systems, 2022

【16】 Visual Named Entity Linking: A New Dataset and A Baseline
标题：可视化命名实体链接：新的数据集和基线
链接：https://arxiv.org/abs/2211.04872
作者：Wenxiang Sun,Yixing Fan,Jiafeng Guo,Ruqing Zhang,Xueqi Cheng
机构：CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, University of Chinese Academy of Sciences, Beijing, China
备注：13 pages, 11 figures, published to EMNLP 2022(findings)

【17】 Leveraging Sequentiality in Reinforcement Learning from a Single Demonstration
标：从单个演示中利用强化学习中的顺序性
链接：https://arxiv.org/abs/2211.04786
作者：Alexandre Chenu,Olivier Serris,Olivier Sigaud,Nicolas Perrin-Gilbert
机构： Obtaining such demonstrations can beprohibitively difficult when dealing with complex systems in 1Sorbonne Universite

【18】 ARNet: Automatic Refinement Network for Noisy Partial Label Learning
标题：ARNet：用于噪声部分标注学习的自动求精网络
链接：https://arxiv.org/abs/2211.04774
作者：Zheng Lian,Mingyu Xu,Lan Chen,Licai Sun,Bin Liu,Jianhua Tao
机构： University ofChinese Academy of Sciences, Jianhua Tao is with National Laboratory of Pattern Recognition

【19】 Continual learning autoencoder training for a particle-in-cell simulation via streaming
标题：用于流传输的细胞内粒子模拟的连续学习自动编码器训练
链接：https://arxiv.org/abs/2211.04770
作者：Patrick Stiller,Varun Makdani,Franz Pschel,Richard Pausch,Alexander Debus,Michael Bussmann,Nico Hoffmann
机构：Institute for Radiation, Helmholtz-Zentrum Dresden-Rossendorf, Center for Advanced Systems Understanding Grlitz, Helmholtz-Zenrum Dresden-Rossendorf

【20】 Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF
标题：医学文本中嵌套命名实体识别：一种具有注意CRF的自适应共享网络体系结构
链接：https://arxiv.org/abs/2211.04759
作者：Junzhe Jiang,Mingyue Cheng,Qi Liu,Zhi Li,Enhong Chen
机构： Anhui Province Key Laboratory of Big Data Analysis and Application, University, of Science and Technology of China, Hefei, China, State Key Laboratory of Cognitive Intelligence, Hefei, China

【21】 Towards Global Crop Maps with Transfer Learning
标题：基于迁移学习的全球作物地图研究
链接：https://arxiv.org/abs/2211.04755
作者：Hyun-Woo Jo,Alkiviadis Koukos,Vasileios Sitokonstantinou,Woo-Kyun Lee,Charalampos Kontoes
机构： Department of Environmental Science and Ecological Engineering, Korea University, BEYOND Centre, IAASARS, National Observatory of Athens
备注：Accepted for publication at Tackling Climate Change with Machine Learning: workshop at NeurIPS 2022

【22】 Efficient Neural Mapping for Localisation of Unmanned Ground Vehicles
标题：无人地面车辆定位的高效神经映射方法
链接：https://arxiv.org/abs/2211.04718
作者：Christopher J. Holder,Muhammad Shafique
机构： New York University Abu Dhabi
备注：This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

【23】 Deep Explainable Learning with Graph Based Data Assessing and Rule Reasoning
标题：基于图的数据评估和规则推理的深度可解释学习
链接：https://arxiv.org/abs/2211.04693
作者：Yuanlong Li,Gaopan Huang,Min Zhou,Chuan Fu,Honglin Qiao,Yan He
机构：Aliyun, Alibaba Group, Hangzhou, China, Xichang Steel and Vanadium Co., Ltd., Pangang Group, Xichang, China

【24】 Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind
标题：电影中的小镜头角色理解作为心理理论元学习的评估
链接：https://arxiv.org/abs/2211.04684
作者：Mo Yu,Yisi Sang,Kangsheng Pu,Zekai Wei,Han Wang,Jing Li,Yue Yu,Jie Zhou
机构：Pattern Recognition Center, WeChat AI, Syracuse University, New Jersey Institute of Technology, Lehigh University

【25】 Syntax-Aware On-the-Fly Code Completion
标题：语法感知的动态代码完成
链接：https://arxiv.org/abs/2211.04673
作者：Wannita Takerngsaksiri,Chakkrit Tantithamthavorn,Yuan-Fang Li
机构： Monash University
备注：14 pages, Under Review at IEEE Transactions on Software Engineering

【26】 A Method to Judge the Style of Classical Poetry Based on Pre-trained Model
标题：一种基于预训练模型的古典诗词风格判定方法
链接：https://arxiv.org/abs/2211.04657
作者：Ziyao Wang,Jiandong Zhang,Jun Ma
机构：Lanzhou University, Lanzhou, China, Shandong University School of Literature, Shangdong, China
备注：4 pages, 2 figures

【27】 LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting
标题：LICO-Net：用于硬件高效关键词检测的线性化卷积网络
链接：https://arxiv.org/abs/2211.04635
作者：Haichuan Yang,Zhaojun Yang,Li Wan,Biqiao Zhang,Yangyang Shi,Yiteng Huang,Ivaylo Enchev,Limin Tang,Raziel Alvarez,Ming Sun,Xin Lei,Raghuraman Krishnamoorthi,Vikas Chandra
机构：Meta AI

【28】 DeepE: a deep neural network for knowledge graph embedding
标题：深度：一种用于知识图嵌入的深度神经网络
链接：https://arxiv.org/abs/2211.04620
作者：Zhu Danhao,Shen Si,Huang Shujian,Yin Chang,Dng Ziqi
机构：Department of Criminal Science and, Technology, Jiangsu Police Institute, Department of Computer Science and, Technology, Nanjing University, School of Economics and Management, Nanjing University of Science and, Department of Computer Information and
备注：10 pages, 5 figures, 7 tables

【29】 StructDiffusion: Object-Centric Diffusion for Semantic Rearrangement of Novel Objects
标题：结构扩散：以对象为中心的新事物语义重组扩散
链接：https://arxiv.org/abs/2211.04604
作者：Weiyu Liu,Tucker Hermans,Sonia Chernova,Chris Paxton
机构： 2University of Utah and NVIDIA

【30】 Learning to Follow Instructions in Text-Based Games
标题：在基于文本的游戏中学习遵循说明
链接：https://arxiv.org/abs/2211.04591
作者：Mathieu Tuli,Andrew C. Li,Pashootan Vaezipoor,Toryn Q. Klassen,Scott Sanner,Sheila A. McIlraith
机构：University of Toronto, Toronto, Canada, Vector Institute for Artificial Intelligence, Toronto, Canada, Schwartz Reisman Institute for Technology and Society, Toronto, Canada
备注：NeurIPS 2022

【31】 Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality
标题：人工智能时代的能源系统数字化：实现碳中和的三层方法
链接：https://arxiv.org/abs/2211.04584
作者：Le Xie,Tong Huang,Xiangtian Zheng,Yan Liu,Mengdi Wang,Vijay Vittal,P. R. Kumar,Srinivas Shakkottai,Yi Cui
机构：Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA, Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA, USA
备注：To be published in Patterns (Cell Press)

【32】 Wall Street Tree Search: Risk-Aware Planning for Offline Reinforcement Learning
标题：华尔街树搜索：线下强化学习的风险意识规划
链接：https://arxiv.org/abs/2211.04583
作者：Dan Elbaz,Gal Novik,Oren Salzman
备注：Accepted to Foundation Models for Decision Making (FMDM) Workshop at 36th Conference on Neural Information Processing Systems (NeurIPS)

【33】 Detecting Euphemisms with Literal Descriptions and Visual Imagery
标题：基于文字描述和视觉意象的委婉语检测法
链接：https://arxiv.org/abs/2211.04576
作者：lker Kesen,Aykut Erdem,Erkut Erdem,Iacer Calixto
机构： Ko University, KUIS AI Center , Ko University, Computer Engineering Department, Hacettepe University, Computer Engineering Department, Amsterdam UMC, University of Amsterdam, Department of Medical Informatics
备注：7 pages, 1 table, 1 figure. Accepted to the 3rd Workshop on Figurative Language Processing at EMNLP 2022. this https URL

【34】 Detecting and Accommodating Novel Types and Concepts in an Embodied Simulation Environment
标题：在具体化仿真环境中检测和适应新类型和新概念
链接：https://arxiv.org/abs/2211.04555
作者：Sadaf Ghaffari,Nikhil Krishnaswamy
机构：Situated Grounding and Natural Language Lab, Department of Computer Science, Colorado State, University, Fort Collins, CO , USA
备注：arXiv admin note: substantial text overlap with arXiv:2204.08107

【35】 ARMOR: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data
标题：ARMOR：一种基于模型的离线数据任意基线策略改进框架
链接：https://arxiv.org/abs/2211.04538
作者：Tengyang Xie,Mohak Bhardwaj,Nan Jiang,Ching-An Cheng
机构：University of Illinois at Urbana-Champaign, University of Washington, Microsoft Research

【36】 Harmonizing the object recognition strategies of deep neural networks with humans
标题：深度神经网络的目标识别策略与人类的协调
链接：https://arxiv.org/abs/2211.04533
作者：Thomas Fel,Ivan Felipe,Drew Linsley,Thomas Serre
机构： 1Department of Cognitive, Brown University, RI 2Artificial and Natural Intelligence Toulouse Institute (ANITI), France 3Carney Institute for Brain Science
备注：Published at NeurIPS 2022

【37】 Knowledge Retrieval for Robotic Cooking
标题：面向机器人烹饪的知识检索
链接：https://arxiv.org/abs/2211.04524
作者：Kundana Mandapaka
机构：University of South Florida
备注：3 pages, 1 figure, and 2 tables

【38】 Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT
标题：慢性疾病中的精神关怀：使用物联网的可解释人工智能方法
链接：https://arxiv.org/abs/2211.04509
作者：Jiaheng Xie,Xiaohang Zhao,Xiang Liu,Xiao Fang
机构： Lerner College of Business and Economics, University of Delaware, Newark, DE, USA, School of Information Management & Engineering, Shanghai University of Finance and Economics, Shanghai, China, Equal Contribution
备注：39 pages, 12 figures

【39】 Active Example Selection for In-Context Learning
标题：情境学习中的主动范例选择
链接：https://arxiv.org/abs/2211.04486
作者：Yiming Zhang,Shi Feng,Chenhao Tan
机构：University of Chicago
：EMNLP 2022, code is available at this https URL

【40】 Discover, Explanation, Improvement: Automatic Slice Detection Framework for Natural Language Processing
标题：发现、解释、改进：面向自然语言处理的自动切片检测框架
链接：https://arxiv.org/abs/2211.04476
作者：Wenyue Hua,Lifeng Jin,Linfeng Song,Haitao Mi,Yongfeng Zhang,Dong Yu
机构：Department of Computer Science, Rutgers University, New Brunswick, Tencent America AI Lab
备注：15 pages, 5 figures

【41】 Towards Improved Room Impulse Response Estimation for Speech Recognition
标题：语音识别中改进的房间脉冲响应估计方法
链接：https://arxiv.org/abs/2211.04473
作者：Anton Ratnarajah,Ishwarya Ananthabhotla,Vamsi Krishna Ithapu,Pablo Hoffmann,Dinesh Manocha,Paul Calamia
机构： University of Maryland, College Park, USA, Reality Labs Research at Meta, Redmond, WA USA

【42】 A Comparative Study of Data Augmentation Techniques for Deep Learning Based Emotion Recognition
标题：基于深度学习的情感识别数据增强技术比较研究
链接：https://arxiv.org/abs/2211.05047
作者：Ravi Shankar,Abdouh Harouna Kenfack,Arjun Somayazulu,Archana Venkataraman
机构：Department of Electrical and Computer Engineering, Johns Hopkins University, USA, Department of Applied Mathematics and Statistics, Johns Hopkins University, USA, Department of Computer Science, Johns Hopkins University, USA
备注：Under Submission

【43】 Quantitative Susceptibility Mapping in Cognitive Decline: A Review of Technical Aspects and Applications
标题：认知衰退的定量易感地形图：技术方面及应用综述
链接：https://arxiv.org/abs/2211.04764
作者：Shradha Verma,Tripti Goel,M Tanveer
机构：Biomedical Imaging Lab, National Institute of Technology Silchar, Assam, India., Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Madhya, Pradesh, India.

【44】 PhaseAug: A Differentiable Augmentation for Speech Synthesis to Simulate One-to-Many Mapping
标题：PhaseAug：一种模拟一对多映射的语音合成可区分增强算法
链接：https://arxiv.org/abs/2211.04610
作者：Junhyeok Lee,Seungu Han,Hyunjae Cho,Wonbin Jung
机构： MINDsLab Inc., Republic of Korea, seoul National University (SNU), Republic of Korea, Korea Advanced Institute of Science and Technology (KAIST), Republic of Korea
备注：Submitted to ICASSP 2023

机器翻译由腾讯交互翻译提供，仅供参考

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。