Author name cluster

Ben Krause

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2 papers

2 author rows

NeurIPS Conference 2021 Conference Paper

Deep Extrapolation for Attribute-Enhanced Generation

Alvin Chan
Ali Madani
Ben Krause
Nikhil Naik

Attribute extrapolation in sample generation is challenging for deep neural networks operating beyond the training distribution. We formulate a new task for extrapolation in sequence generation, focusing on natural language and proteins, and propose GENhance, a generative framework that enhances attributes through a learned latent space. Trained on movie reviews and a computed protein stability dataset, GENhance can generate strongly-positive text reviews and highly stable protein sequences without being exposed to similar data during training. We release our benchmark tasks and models to contribute to the study of generative modeling extrapolation and data-driven design in biology and chemistry.

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ICML Conference 2018 Conference Paper

Dynamic Evaluation of Neural Sequence Models

Ben Krause
Emmanuel Kahembwe
Iain Murray 0001
Steve Renals

We explore dynamic evaluation, where sequence models are adapted to the recent sequence history using gradient descent, assigning higher probabilities to re-occurring sequential patterns. We develop a dynamic evaluation approach that outperforms existing adaptation approaches in our comparisons. We apply dynamic evaluation to outperform all previous word-level perplexities on the Penn Treebank and WikiText-2 datasets (achieving 51. 1 and 44. 3 respectively) and all previous character-level cross-entropies on the text8 and Hutter Prize datasets (achieving 1. 19 bits/char and 1. 08 bits/char respectively).

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