Event
Intuitive Understanding of Generalization in Neural Networks - Jimmy Ba, Assistant Professor, University of Toronto

Jimmy Ba Image

DATE: Fri, October 23, 2020 - 3:00 pm

LOCATION: Please register to receive the Zoom link

DETAILS

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Abstract:

How does a large-scale neural network with millions of parameters generalize from only a few training examples? How does one learning algorithm generalize better than another? In this talk, I will discuss some of our recent work and try to give intuitive answers to these questions. First, I will present the intuitions about the generalization properties of two-layer neural networks in high-dimensions, i.e., when the number of training examples, input features, and hidden neurons tends to infinity the same rate. In the second half of the talk, I will describe some intuitions about the trade-offs in generalization between various learning algorithms.

 

Bio:

Jimmy Ba is an Assistant Professor in the Department of Computer Science at the University of Toronto. He is also a faculty member at Vector Institute, the Canadian Institute for Advanced Research (CIFAR) AI Chair program, and was a recipient of Facebook Graduate Fellowship 2016 in machine learning. Jimmy’s research focuses on developing novel deep learning algorithms, helping to advance our understanding of the human mind, intelligence, and computation. 


 

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