Ulysses Sequence Parallelism: Training with Million-Token Contexts

How an MIT research project became a global programming language

With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers

When AI art has no author: Study finds generated images often can’t be traced to training data

A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.