Interpretable Recurrent Neural Networks Using Sequential Sparse Recovery

Autor:	Wisdom, Scott, Powers, Thomas, Pitton, James, Atlas, Les
Rok vydání:	2016
Předmět:	Statistics - Machine Learning Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	Recurrent neural networks (RNNs) are powerful and effective for processing sequential data. However, RNNs are usually considered "black box" models whose internal structure and learned parameters are not interpretable. In this paper, we propose an interpretable RNN based on the sequential iterative soft-thresholding algorithm (SISTA) for solving the sequential sparse recovery problem, which models a sequence of correlated observations with a sequence of sparse latent vectors. The architecture of the resulting SISTA-RNN is implicitly defined by the computational structure of SISTA, which results in a novel stacked RNN architecture. Furthermore, the weights of the SISTA-RNN are perfectly interpretable as the parameters of a principled statistical model, which in this case include a sparsifying dictionary, iterative step size, and regularization parameters. In addition, on a particular sequential compressive sensing task, the SISTA-RNN trains faster and achieves better performance than conventional state-of-the-art black box RNNs, including long-short term memory (LSTM) RNNs. Comment: Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1611.07252 Zobrazit plný text záznamu View this record from Arxiv