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Exploring Infinite-Width Limit of Deep Neural Networks

  • Jaehoon Lee (Google Brain)
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Abstract

In this talk, I will discuss our research on understanding the infinite-width limit of neural networks. In this limit, neural networks correspond to Neural Network Gaussian Processes (NNGPs) and Neural Tangent Kernels (NTKs). I will first describe our empirical study exploring the relationship between wide neural networks and neural kernel methods. Our study resolves a variety of open questions related to infinitely wide neural networks and opens up new interesting questions. In the second half of the talk, I will discuss our recent work on scaling up infinite-width neural kernel methods to millions of data points. There are unique challenges in scaling up neural kernel methods, and I will talk about our attempts to overcome them. If there is time, I will discuss some applications of the infinite-width limit of neural networks, such as dataset distillation, neural architecture search, uncertainty quantification, and neural scaling laws.

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seminar
25.04.24 16.05.24

Math Machine Learning seminar MPI MIS + UCLA

MPI for Mathematics in the Sciences Live Stream

Katharina Matschke

MPI for Mathematics in the Sciences Contact via Mail

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