Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks

Ramin Hasani, Alexander Amini, Mathias Lechner, Felix Naser, Radu Grosu, Daniela Rus
2019 2019 International Joint Conference on Neural Networks (IJCNN)  
In this paper, we introduce a novel method to interpret recurrent neural networks (RNNs), particularly long short-term memory networks (LSTMs) at the cellular level. We propose a systematic pipeline for interpreting individual hidden state dynamics within the network using response characterization methods. The ranked contribution of individual cells to the network's output is computed by analyzing a set of interpretable metrics of their decoupled step and sinusoidal responses. As a result, our
more » ... method is able to uniquely identify neurons with insightful dynamics, quantify relationships between dynamical properties and test accuracy through ablation analysis, and interpret the impact of network capacity on a network's dynamical distribution. Finally, we demonstrate generalizability and scalability of our method by evaluating a series of different benchmark sequential datasets.
doi:10.1109/ijcnn.2019.8851954 dblp:conf/ijcnn/HasaniALNGR19 fatcat:xm6iqrc5tnhslisqvcfgde6xva