One of the most difficult speech recognition tasks is accurate recognition of human to human
communication. Advances in deep learning over the last few years have produced major …

Recent advances in machine learning have leveraged evolutionary information in multiple
sequence alignments to predict protein structure. We demonstrate direct inference of full …

In the field of artificial intelligence, a combination of scale in data and model capacity
enabled by unsupervised learning has led to major advances in representation learning and …

Modeling the effect of sequence variation on function is a fundamental problem for
understanding and designing proteins. Since evolution encodes information about function into …

Unsupervised protein language models trained across millions of diverse sequences learn
structure and function of proteins. Protein language models studied to date have been …

We consider the problem of predicting a protein sequence from its backbone atom coordinates.
Machine learning approaches to this problem to date have been limited by the number of …

Large language models have recently been shown to develop emergent capabilities with
scale, going beyond simple pattern matching to perform higher level reasoning and generate …

The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical
repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report …

Unsupervised contact prediction is central to uncovering physical, structural, and functional
constraints for protein structure determination and design. For decades, the predominant …

Convolutional neural networks (CNNs) are a standard component of many current state-of-the-art
Large Vocabulary Continuous Speech Recognition (LVCSR) systems. However, …