icml_2018_notes_David_Abel.pdf
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2024-04-25
Learning
Deep
Theory
Optimization
Tutorial
Learn
机器
Control
112.2
Conclusion
9.6 MB
ICML 2018 Notes
Stockholm, Sweden
David Abel∗
david_abel@brown.edu
July 2018
Contents
1 Conference Highlights 3
2 Tuesday July 10th 3
2.1 Tutorial: Toward Theoretical Understanding of Deep Learning . . . . . . . . . . . . 4
2.1.1 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.2 Overparameterization and Generalization Theory . . . . . . . . . . . . . . . . 6
2.1.3 The Role of Depth in Deep Learning . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.4 Theory of Generative Models and Adversarial Nets . . . . . . . . . . . . . . . 9
2.1.5 Deep Learning Free . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Tutorial: Optimization Perspectives on Learning to Control . . . . . . . . . . . . . . 11
2.2.1 Introduction: RL, Optimzation, and Control . . . . . . . . . . . . . . . . . . 11
2.2.2 Different Approaches to Learn
Learning/Deep/Theory/Optimization/Tutorial/Learn/机器/Control/112.2/Conclusion/
Learning/Deep/Theory/Optimization/Tutorial/Learn/机器/Control/112.2/Conclusion/
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