State gradients for analyzing memory in LSTM language models
Authors:
- Lyan VerwimpLVLyan Verwimp
- Hugo Van HammeHHHugo Van Hamme
- Patrick WambacqPWPatrick Wambacq
- 1 May 2020
- journal article
- research article
- Published by Elsevier in Computer Speech & Language
- Vol. 61
Abstract
No abstract availableKeywords
Funding Information
- Agentschap voor Innovatie door Wetenschap en Technologie
This publication has 6 references indexed in Scilit:
- Representation of Linguistic Form and Function in Recurrent Neural NetworksComputational Linguistics, 2017
- LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural NetworksIEEE Transactions on Visualization and Computer Graphics, 2017
- Assessing the Ability of LSTMs to Learn Syntax-Sensitive DependenciesTransactions of the Association for Computational Linguistics, 2016
- LSTM recurrent networks learn simple context-free and context-sensitive languagesIEEE Transactions on Neural Networks, 2001
- Long Short-Term MemoryNeural Computation, 1997
- Finding Structure in TimeCognitive Science, 1990