Siddharth Goyal

TL;DR: This work uses unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity, enabling state-of-the-art supervised prediction of mutational effect and secondary structure, and improving state- of- the-art features for long-range contact prediction.

TL;DR: This paper used unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity, which contains information about biological properties in its representations.

TL;DR: This work creates a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages and explores how to effectively increase model capacity through a combination of dense scaling and language-specific sparse parameters to create high quality models.

TL;DR: In this article, the authors proposed several control algorithms and compared their performance and complexity through simulations; the control algorithms regulate the indoor climate of commercial buildings and use occupancy information to reduce energy use while maintaining thermal comfort and indoor air quality.