How symmetry can come to the aid of machine learning
Exploiting the symmetry within datasets, MIT researchers show, can decrease the amount of data needed for training neural networks.
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Exploiting the symmetry within datasets, MIT researchers show, can decrease the amount of data needed for training neural networks.
Hundreds of participants from around the world joined the sixth annual MIT Policy Hackathon to develop data-informed policy solutions to challenges in health, housing, and more.
MIT CSAIL researchers develop advanced machine-learning models that outperform current methods in detecting pancreatic ductal adenocarcinoma.
A multimodal system uses models trained on language, vision, and action data to help robots develop and execute plans for household, construction, and manufacturing tasks.
MIT researchers propose “PEDS” method for developing models of complex physical systems in mechanics, optics, thermal transport, fluid dynamics, physical chemistry, climate, and more.
Master's students Irene Terpstra ’23 and Rujul Gandhi ’22 use language to design new integrated circuits and make it understandable to robots.
“Minimum viewing time” benchmark gauges image recognition complexity for AI systems by measuring the time needed for accurate human identification.
The graduate students will aim to commercialize innovations in AI, machine learning, and data science.
MIT researchers develop a customized onboarding process that helps a human learn when a model’s advice is trustworthy.
By analyzing bacterial data, researchers have discovered thousands of rare new CRISPR systems that have a range of functions and could enable gene editing, diagnostics, and more.