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20162019
most citedRegularizing and Optimizing LSTM Language Models

466 citations · 582 across the 4 of their papers we have counts for

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5 papers · 1 filter

cs.CL201962 cited

Single Headed Attention RNN: Stop Thinking With Your Head

Stephen Merity

The leading approaches in language modeling are all obsessed with TV shows of my youth - namely Transformers and Sesame Street. Transformers this, Transformers that, and over here…

cs.CL2018

An Analysis of Neural Language Modeling at Multiple Scales

Stephen Merity, Nitish Shirish Keskar, Richard Socher

Many of the leading approaches in language modeling introduce novel, complex and specialized architectures. We take existing state-of-the-art word level language models based on LS…

cs.CL201711 cited

A Flexible Approach to Automated RNN Architecture Generation

Martin Schrimpf, Stephen Merity, James Bradbury +1

The process of designing neural architectures requires expert knowledge and extensive trial and error. While automated architecture search may simplify these requirements, the recu…

cs.CL2017466 cited

Regularizing and Optimizing LSTM Language Models

Stephen Merity, Nitish Shirish Keskar, Richard Socher

Recurrent neural networks (RNNs), such as long short-term memory networks (LSTMs), serve as a fundamental building block for many sequence learning tasks, including machine transla…

cs.CL201743 cited

Revisiting Activation Regularization for Language RNNs

Stephen Merity, Bryan McCann, Richard Socher

Recurrent neural networks (RNNs) serve as a fundamental building block for many sequence tasks across natural language processing. Recent research has focused on recurrent dropout…