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

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0302-9743 ge 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

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620554.jpg

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978-3-031-43423-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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MMA: Multi-Metric-Autoencoder for?Analyzing High-Dimensional and?Incomplete Dataa better representation from a set of dispersed metric spaces. Extensive experiments on four real-world datasets demonstrate that our MMA significantly outperforms seven state-of-the-art models. Our code is available at the link

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Exploring and?Exploiting Data-Free Model Stealing (i) substitute model which imitates the target model through synthetic queries and their inferred labels; and (ii) a tandem generator consisting of two networks, . and ., which first explores the synthetic data space via . and then exploits high-quality examples via . to maximize the knowledge tran

蓋他為秘密

Exploring the?Training Robustness of?Distributional Reinforcement Learning Against Noisy State Obser distributional RL loss based on the categorical parameterization equipped with the Kullback-Leibler?(KL) divergence. The resulting stable gradients while the optimization in distributional RL accounts for its better training robustness against state observation noises. Finally, extensive experiment

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