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20182025
most citedPopulation-Based Black-Box Optimization for Biological Sequence Design

93 citations · 189 across the 7 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

cs.LG2020

Latent Programmer: Discrete Latent Codes for Program Synthesis

Joey Hong, David Dohan, Rishabh Singh +2

In many sequence learning tasks, such as program synthesis and document summarization, a key problem is searching over a large space of possible output sequences. We propose to lea…

q-bio.BM202033 cited

Is Transfer Learning Necessary for Protein Landscape Prediction?

Amir Shanehsazzadeh, David Belanger, David Dohan

Recently, there has been great interest in learning how to best represent proteins, specifically with fixed-length embeddings. Deep learning has become a popular tool for protein r…

q-bio.BM2020

Fixed-Length Protein Embeddings using Contextual Lenses

Amir Shanehsazzadeh, David Belanger, David Dohan

The Basic Local Alignment Search Tool (BLAST) is currently the most popular method for searching databases of biological sequences. BLAST compares sequences via similarity defined…

cs.LG202093 cited

Population-Based Black-Box Optimization for Biological Sequence Design

Christof Angermueller, David Belanger, Andreea Gane +5

The use of black-box optimization for the design of new biological sequences is an emerging research area with potentially revolutionary impact. The cost and latency of wet-lab exp…

cs.LG202029 cited

Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers

Krzysztof Choromanski, Valerii Likhosherstov, David Dohan +8

Transformer models have achieved state-of-the-art results across a diverse range of domains. However, concern over the cost of training the attention mechanism to learn complex dep…