A theory of the dynamics of deep learning: Consequences for semantic development
Abstract: Anatomically, the brain is deep; and computationally, deep learning is known to be hard. How might depth impact learning in the brain? To understand the specific ramifications of depth, I develop the theory of learning in deep linear neural networks. I give exact solutions to the dynamics of learning which specify how every weight in the network evolves over the course of training. The theory answers fundamental questions such as how learning speed scales with depth, and why unsupervised pretraining accelerates learning. Turning to generalization error, I use random matrix theory to analyze the cognitively-relevant "high-dimensional" regime, where the number of training examples is on the order or even less than the number of adjustable synapses. Consistent with the striking performance of very large deep network models in practice, I show that good generalization is possible in overcomplete networks due to implicit regularization in the dynamics of gradient descent. These results reveal a speed-accuracy trade-off between training speed and generalization performance in deep networks. Drawing on these findings, I then describe an application to human semantic development. From a stream of individual episodes, we abstract our knowledge of the world into categories and overarching structures like trees and hierarchies. I present an exactly solvable model of this process by considering how a deep network will learn about richly structured environments specified as probabilistic graphical models. This scheme illuminates empirical phenomena documented by developmental psychologists, such as transient illusory correlations and changing patterns of inductive generalization. Neurally, the theory suggests that the representation of complex structures resides in the similarity structure of neural population responses, not the detailed activity patterns of individual neurons. Overall, these results suggest that depth may be an important computational principle influencing learning in the brain. Deep linear networks yield a tractable theory of layered learning that interlinks computation, neural representations, and behavior.
About the speaker:
Dr. Andrew Saxe is a Swartz Postdoctoral Fellow in Theoretical Neuroscience at Harvard University. He completed his PhD in Electrical Engineering at Stanford University, advised by Jay McClelland, Surya Ganguli, Andrew Ng, and Christoph Schreiner. His dissertation received the Robert J. Glushko Dissertation Prize from the Cognitive Science Society. His research focuses on the theory of deep learning and its applications to phenomena in neuroscience and psychology.