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Titlebook: Machine Learning and Knowledge Discovery in Databases: Research Track; European Conference, Danai Koutra,Claudia Plant,Francesco Bonchi Con

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书目名称Machine Learning and Knowledge Discovery in Databases: Research Track
副标题European Conference,
编辑Danai Koutra,Claudia Plant,Francesco Bonchi
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Machine Learning and Knowledge Discovery in Databases: Research Track; European Conference, Danai Koutra,Claudia Plant,Francesco Bonchi Con
描述The multi-volume set LNAI 14169 until  14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023..The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. .The volumes are organized in topical sections as follows:.Part I:. Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality;   Clustering..Part II: .​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning..Part III: .​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning..Part IV:. ​Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning..Part V:. ​Robustness; Time Series; Transfer and Multitask Learning..Part VI:. ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Intera
出版日期Conference proceedings 2023
关键词artificial intelligence; computer hardware; computer networks; computer security; computer systems; compu
版次1
doihttps://doi.org/10.1007/978-3-031-43415-0
isbn_softcover978-3-031-43414-3
isbn_ebook978-3-031-43415-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Machine Learning and Knowledge Discovery in Databases: Research Track978-3-031-43415-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Scoring Rule Nets: Beyond Mean Target Prediction in Multivariate Regressioncorrelation. We then show in a variety of experiments on both synthetic and real data, that Conditional CRPS often outperforms MLE, and produces results comparable to state-of-the-art non-parametric models, such as Distributional Random Forest (DRF).
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Rényi Divergence Deep Mutual Learningll reach nearby local optima but continue searching within a bounded scope, which may help mitigate overfitting. Finally, our extensive empirical results demonstrate the advantage of combining DML and the Rényi divergence, leading to further improvement in model generalization.
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