Year: 2019
Type: preprint
Abstract: There is an implicit assumption that by unfolding recurrent neural networks (RNN) in finite time, the misspecification of choosing a zero value for the initial hidden state is mitigated by later time steps. This assumption has been shown to work in practice and alternative initialization may be suggested but often overlooked. In this paper, we propose a method of parameterizing the initial hidden state of an RNN. The resulting architecture, referred to as a Contextual RNN, can be trained end-to-end. The performance on an associative retrieval task is found to improve by conditioning the RNN initial hidden state on contextual information from the input sequence. Furthermore, we propose a novel method of conditionally generating sequences using the hidden state parameterization of Contextual RNN.
Source: arXiv (Cornell University)
Authors: Wenke, Sam, Fleming, Jim
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2924793069