Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck Languages

8 November 2019

Papers citing "Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck Languages"

8 / 8 papers shown

Title
Training Neural Networks as Recognizers of Formal Languages Alexandra Butoi Ghazal Khalighinejad Anej Svete Josef Valvoda Ryan Cotterell Brian DuSell NAI 44 2 0 11 Nov 2024
Separations in the Representational Capabilities of Transformers and Recurrent Architectures S. Bhattamishra Michael Hahn Phil Blunsom Varun Kanade GNN 44 9 0 13 Jun 2024
Learning Universal Predictors Jordi Grau-Moya Tim Genewein Marcus Hutter Laurent Orseau Grégoire Delétang ... Anian Ruoss Wenliang Kevin Li Christopher Mattern Matthew Aitchison J. Veness 27 11 0 26 Jan 2024
Theoretical Conditions and Empirical Failure of Bracket Counting on Long Sequences with Linear Recurrent Networks Nadine El-Naggar Pranava Madhyastha Tillman Weyde 14 1 0 07 Apr 2023
Minimum Description Length Recurrent Neural Networks N. Lan Michal Geyer Emmanuel Chemla Roni Katzir 16 12 0 31 Oct 2021
Causal Transformers Perform Below Chance on Recursive Nested Constructions, Unlike Humans Yair Lakretz T. Desbordes Dieuwke Hupkes S. Dehaene 233 11 0 14 Oct 2021
Formal Language Theory Meets Modern NLP William Merrill AI4CE NAI 16 12 0 19 Feb 2021
Can RNNs learn Recursive Nested Subject-Verb Agreements? Yair Lakretz T. Desbordes J. King Benoît Crabbé Maxime Oquab S. Dehaene 160 19 0 06 Jan 2021