Towards a Theoretical Understanding of Hashing-Based Neural Nets
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:127-137, 2019.
Parameter reduction has been a popular topic in deep learning due to the ever- increasing size of deep neural network models and the need to train and run deep neural nets on resource limited machines. Despite many efforts in this area, there were no rigorous theoretical guarantees on why existing neural net compression methods should work. In this paper, we provide provable guarantees on some hashing-based parameter reduction methods in neural nets. First, we introduce a neural net compression scheme based on random linear sketching (which is usually implemented efficiently via hashing), and show that the sketched (smaller) network is able to approximate the original network on all input data coming from any smooth well-conditioned low-dimensional manifold. The sketched network can also be trained directly via back-propagation. Next, we study the previously proposed HashedNets architecture and show that the optimization landscape of one-hidden-layer HashedNets has a local strong convexity property similar to a normal fully connected neural network. Together with the initialization algorithm developed in , this implies that the parameters in HashedNets can be provably recovered. We complement our theoretical results with some empirical verification.