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On the Connection between Neural Networks and Kernels: a Modern Perspective -Simon Du
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Classroom Contents
Theory of Deep Learning - Where Next?
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- 1 Is Optimization the Right Language to Understand Deep Learning? - Sanjeev Arora
- 2 Emergent linguistic structure in deep contextual neural word representations - Chris Manning
- 3 Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets - Rong Ge
- 4 Fixing GAN optimization through competitive gradient descent - Anima Anandkumar
- 5 Tightening information-theoretic generalization bounds with data-dependent estimate... - Daniel Roy
- 6 Spotlight Talks - Amir Asadi, Dimitris Kalimeris
- 7 PAC-Bayesian approaches to understanding generalization in deep learning - Gintare Dziugaite
- 8 Overcoming the Curse of Dimensionality and Mode Collapse - Ke Li
- 9 Are All Features Created Equal? - Aleksander Madry
- 10 Energy-based Approaches to Representation Learning - Yann LeCun
- 11 On Large Deviation Principles for Large Neural Networks - Joan Bruna
- 12 Neural Models for Speech and Language: Successes, Challenges, and the... - Michael Collins
- 13 Spotlight Talks - Various
- 14 On the Connection between Neural Networks and Kernels: a Modern Perspective -Simon Du
- 15 From Classical Statistics to Modern ML: the Lessons of Deep Learning - Mikhail Belkin
- 16 Spotlight Talks - Various
- 17 Towards a theoretical foundation of neural networks - Jason Lee
- 18 Panel Session - Various
- 19 Learning Representations Using Causal Invariance - Leon Bottou
- 20 Understanding the inductive bias due to dropout - Raman Arora
- 21 Interpreting Deep Neural Networks - Bin Yu
- 22 Kernel and Rich Regimes in Deep Learning - Nati Srebro
- 23 Spotlight Talks Pt1: Jiaoyang Huang, Arjun Nitin Bhagoji, Rosemary Ke
- 24 Spotlight Talks Pt2 - Sebastian Goldt, Akshay Rangamani, Omar Shehab, Or Sharir
- 25 Provably Efficient Reinforcement Learning with Linear Function Approximation - Chi Jin
- 26 Reinforcement Learning, Deep Learning,and the Role of Policy Gradient Methods - Sham Kakade
- 27 Statistical mechanics of deep learning - Surya Ganguli
- 28 Representational Power of Graph Neural Networks - Stefanie Jegelka
- 29 Spotlight Talks Pt1 - Zhiyuan Li, John Zarka, Stanislav Fort
- 30 Toward a Causal Analysis of Generalization in Deep Learning - Behnam Neyshabur
- 31 Spotlight Talks Pt2 - Zhifeng Kong, Daniel Paul Kunin, Omar Montasser
- 32 Designing explicit regularizers for deep models? - Tengyu Ma
