Speeding up drug discovery with diffusion generative models
MIT researchers built DiffDock, a model that may one day be able to find new drugs faster than traditional methods and reduce the potential for adverse side effects.
MIT researchers built DiffDock, a model that may one day be able to find new drugs faster than traditional methods and reduce the potential for adverse side effects.
With the right building blocks, machine-learning models can more accurately perform tasks like fraud detection or spam filtering.
New LiGO technique accelerates training of large machine-learning models, reducing the monetary and environmental cost of developing AI applications.
J-WAFS researchers are using remote sensing observations to build high-resolution systems to monitor drought.
Computational chemists design better ways of discovering and designing materials for energy applications.
Researchers used machine learning to build faster and more efficient hash functions, which are a key component of databases.
The computer science and philosophy double-major aims to advance the field of AI ethics.
Aided by machine learning, scientists are working to develop a vaccine that would be effective against all SARS-CoV-2 strains.
MIT researchers uncover the structural properties and dynamics of deep classifiers, offering novel explanations for optimization, generalization, and approximation in deep networks.
MIT researchers trained logic-aware language models to reduce harmful stereotypes like gender and racial biases.