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上海国际网站建设,怎样做网站软件,本公司经营网站建设,二级子域名查询原标题#xff1a;机器学习领域各领域必读经典综述论文整理分享机器学习是一门多领域交叉学科#xff0c;涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。专门研究计算机怎样模拟或实现人类的学习行为#xff0c;以获取新的知识或技能#xff0c;重新组织…原标题机器学习领域各领域必读经典综述论文整理分享机器学习是一门多领域交叉学科涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。专门研究计算机怎样模拟或实现人类的学习行为以获取新的知识或技能重新组织已有的知识结构使之不断改善自身的性能。机器学习及其相关领域如深度学习、自然语言处理、计算机视觉、推荐系统、强化学习等领域最近几年非常火每年各式各样的国际顶会投稿数每年都会海量增加。要持续Follow这些领域最新的技术刷遍各大会议最新会议非常费时费力特别是对于刚入门的同学。因此为了方便同学们了解机器学习、AI各领域的最新的技术全貌本资源整理了各领域必读的经典综述论文分享给大家。资源整理自网络源地址https://github.com/eugeneyan/ml-surveys目录推荐系统Algorithms: Recommender systems surveyAlgorithms: Deep Learning based Recommender System: A Survey and New PerspectivesAlgorithms: Are We Really Making Progress? A Worrying Analysis of Neural Recommendation ApproachesSerendipity: A Survey of Serendipity in Recommender SystemsDiversity: Diversity in Recommender Systems – A surveyExplanations: A Survey of Explanations in Recommender Systems深度学习Architecture: A State-of-the-Art Survey on Deep Learning Theory and ArchitecturesKnowledge distillation: Knowledge Distillation: A SurveyModel compression: Compression of Deep Learning Models for Text: A SurveyTransfer learning: A Survey on Deep Transfer LearningNeural architecture search: A Comprehensive Survey of Neural Architecture Search-- Challenges and SolutionsNeural architecture search: Neural Architecture Search: A Survey自然语言处理Deep Learning: Recent Trends in Deep Learning Based Natural Language ProcessingClassification: Deep Learning Based Text Classification: A Comprehensive ReviewGeneration: Survey of the SOTA in Natural Language Generation: Core tasks, applications and evaluationGeneration: Neural Language Generation: Formulation, Methods, and EvaluationTransfer learning: Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer (Paper)Metrics: Beyond Accuracy: Behavioral Testing of NLP Models with CheckListMetrics: Evaluation of Text Generation: A Survey计算机视觉Object detection: Object Detection in 20 YearsAdversarial attacks: Threat of Adversarial Attacks on Deep Learning in Computer VisionAutonomous vehicles: Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art深度强化学习Algorithms: A Brief Survey of Deep Reinforcement LearningTransfer learning: Transfer Learning for Reinforcement Learning DomainsEconomics: Review of Deep Reinforcement Learning Methods and Applications in Economics向量化技术Graph: A Comprehensive Survey of Graph Embedding: Problems, Techniques and ApplicationsText: From Word to Sense Embeddings:A Survey on Vector Representations of MeaningText: Diachronic Word Embeddings and Semantic ShiftsText: Word Embeddings: A SurveyMeta-learning and Few-shot LearningNLP: Meta-learning for Few-shot Natural Language Processing: A SurveyDomain Agnostic: Learning from Few Samples: A SurveyNN: Meta-Learning in Neural Networks: A SurveyDomain Agnostic: A Comprehensive Overview and Survey of Recent Advances in Meta-LearningDomain Agnostic: Baby steps towards few-shot learning with multiple semanticsDomain Agnostic: Meta-Learning: A SurveyDomain Agnostic: A Perspective View And Survey Of Meta-learning迁移学习Transfer learning: A Survey on Transfer Learning免责申明:本站所有内容均来自网络,我们对文中观点保持中立,对所包含内容的准确性,可靠性或者完整性不提供任何明示或暗示的保证,请仅作参考。若有侵权,请联系删除。文章来源深度学习与NLP返回搜狐查看更多责任编辑

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