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

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 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

Learning Long-Term Dependencies is Not as Difficult With NARX Recurrent Neural Networks

It has recently been shown that gradient descent learning algorithms for recurrent neural networks can perform poorly on tasks that involve long- term dependencies, i.e. those problems for which the desired output depends on inputs presented at times far in the past. In this paper we explore the long-term dependencies problem for a class of architectures called NARX recurrent neural networks, which have power ful representational capabilities. We have previously reported that gradient descent learning is more effective in NARX networks than in recurrent neural network architectures that have ``hidden states'' on problems includ ing grammatical inference and nonlinear system identification. Typically, the network converges much faster and generalizes better than other net works. The results in this paper are an attempt to explain this phenomenon. We present some experimental results which show that NARX networks can often retain information for two to three times as long as conventional recurrent neural networks. We show that although NARX networks do not circumvent the problem of long-term dependencies, they can greatly improve performance on long-term dependency problems. We also describe in detail some of the assumption regarding what it means to latch information robustly and suggest possible ways to loosen these assumptions. (Also cross-referenced as UMIACS-TR-95-78