Search Results for author: Roshan Rao

Found 8 papers, 5 papers with code

Language models enable zero-shot prediction of the effects of mutations on protein function

1 code implementation NeurIPS 2021 Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu, Alex Rives

Modeling the effect of sequence variation on function is a fundamental problem for understanding and designing proteins.

MSA Transformer

1 code implementation13 Feb 2021 Roshan Rao, Jason Liu, Robert Verkuil, Joshua Meier, John F. Canny, Pieter Abbeel, Tom Sercu, Alexander Rives

Unsupervised protein language models trained across millions of diverse sequences learn structure and function of proteins.

Masked Language Modeling Multiple Sequence Alignment +1

Transformer protein language models are unsupervised structure learners

no code implementations ICLR 2021 Roshan Rao, Joshua Meier, Tom Sercu, Sergey Ovchinnikov, Alexander Rives

Unsupervised contact prediction is central to uncovering physical, structural, and functional constraints for protein structure determination and design.

Language Modelling

ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations

2 code implementations26 Oct 2019 Daniel Seita, David Chan, Roshan Rao, Chen Tang, Mandi Zhao, John Canny

Learning from demonstrations is a popular tool for accelerating and reducing the exploration requirements of reinforcement learning.

Atari Games Q-Learning +2

Evaluating Protein Transfer Learning with TAPE

5 code implementations NeurIPS 2019 Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, Yun S. Song

Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cost of acquiring supervised protein labels, but the current literature is fragmented when it comes to datasets and standardized evaluation techniques.

BIG-bench Machine Learning Representation Learning +1

t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data

1 code implementation31 Jul 2018 David M. Chan, Roshan Rao, Forrest Huang, John F. Canny

Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples.

Dimensionality Reduction

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