Maximum entropy methods for extracting the learned features of deep neural networks.

ABSTRACT: New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints

SUBMITTER: Finnegan A 

PROVIDER: S-EPMC5679649 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature