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Record Nr. |
UNINA9910746283403321 |
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Titolo |
Machine Learning and Knowledge Discovery in Databases : Research Track / / edited by Danai Koutra [and four others] |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2023] |
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©2023 |
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ISBN |
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Edizione |
[First edition.] |
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Descrizione fisica |
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1 online resource (506 pages) |
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Collana |
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Lecture Notes in Computer Science Series ; ; Volume 14173 |
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Disciplina |
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Soggetti |
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Data mining |
Databases |
Machine learning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Intro -- Preface -- Organization -- Invited Talks Abstracts -- Neural Wave Representations -- Physics-Inspired Graph Neural Networks -- Mapping Generative AI -- Contents - Part V -- Robustness -- MMA: Multi-Metric-Autoencoder for Analyzing High-Dimensional and Incomplete Data -- 1 Introduction -- 2 Related Work -- 2.1 LFA-Based Model -- 2.2 Deep Learning-Based Model -- 3 Methodology -- 3.1 Establishment of Base Model -- 3.2 Self-Adaptively Aggregation -- 3.3 Theoretical Analysis -- 4 Experiments -- 4.1 General Settings -- 4.2 Performance Comparison (RQ.1) -- 4.3 The Self-ensembling of MMA (RQ.2) -- 4.4 Base Models' Latent Factors Distribution (RQ. 3) -- 5 Conclusion -- References -- Exploring and Exploiting Data-Free Model Stealing -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 TandemGAN Framework -- 3.2 Optimization Objectives and Training Procedure -- 4 Evaluation -- 4.1 Model Stealing Performance -- 4.2 Ablation Study -- 5 Possible Extension -- 6 Conclusion -- References -- Exploring the Training Robustness of Distributional Reinforcement Learning Against Noisy State Observations -- 1 Introduction -- 2 Background: Distributional RL -- 3 Tabular Case: State-Noisy MDP -- 3.1 Analysis of SN-MDP for Expectation-Based RL -- 3.2 Analysis of SN-MDP in Distributional RL -- 4 Function Approximation Case -- 4.1 Convergence of Linear TD Under Noisy States -- 4.2 Vulnerability of |
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