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U of T Study Suggests Larger Datasets Aren t Always Better for Training AI Models – India Education | Latest Education News | Global Educational News

A new study by researchers at the University of Toronto suggests that one of the fundamental assumptions of deep learning artificial intelligence models – that they require enormous amounts of training data to make accurate predictions – may not be a

Jason hattrickKangming liUniversity of torontoNature communicationsApplied scienceOpen catalyst projectMaterials projectOpen quantum materials database

New study finds bigger datasets might not always be better for AI models

New study finds bigger datasets might not always be better for AI models
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Kangming liJason hattrickNature communicationsUniversity of toronto engineeringToronto engineeringJason hattrick simpersOpen catalyst projectMaterials projectOpen quantum materials database

Toronto Study: Bigger Datasets Not Always Better for AI

Toronto Study: Bigger Datasets Not Always Better for AI
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Kangming liJason hattrickNature communicationsUniversity of toronto engineeringToronto engineeringJason hattrick simpersOpen catalyst projectMaterials projectOpen quantum materials database

AI for Sustainability: Materials Informatics is Driving Change, Finds IDTechEx

Materials informatics is the application of data-driven methods, including machine learning techniques, to the field of materials science.

Osaka universityGreen hydrogen productionGerman aerospace centerCarnegie mellon university open catalystMunich technical universityToray industriesCarnegie mellon universityOpen catalyst projectHydrogen productionElectrolyzer markets

Facebook parent Meta will release calayst data to help make hydrogen fuel more cheaply

Facebook parent Meta will release calayst data to help make hydrogen fuel more cheaply
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