We are the National AI Institute for Foundations of Machine Learning (IFML)
Designated by the National Science Foundation (NSF) in 2020, IFML develops the key foundational tools for the next decade of AI innovation. Our institute comprises researchers from The University of Texas at Austin, University of Washington, Wichita State University, Microsoft Research. Stanford University, Santa Fe Institute, University of California, Los Angeles, University of California, Berkeley, California Institute of Technology, and Arizona State University.
Our researchers create new algorithms that can help machines learn on the fly, change their expectations as they encounter people and objects in real life, and even bounce back from deliberate attempts by adversaries to manipulate datasets.
Featured
IFML Seminar
IFML Seminar: 11/1/24 - Tensor Networks and Phase Transitions in Machine Learning
Speaker Bio: Cristopher Moore received his B.A. in Physics, Mathematics, and Integrated Science from Northwestern University, and his Ph.D. in Physics from Cornell. From 2000 to 2012 he was a professor at the University...
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New IFML Framework for Diffusion Models to be included in several production pipelines by teams in Google
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IFML Diffusion Seminar Series: Tutorial on the Mathematical Foundations of Diffusion Models for Image Generation
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Turbocharging Protein Engineering with AI
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Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension earns Best Paper Award at COLT 2024!
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IFML research collaborations power next generation of multimodal AI with OpenCLIP and DataComp
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NLP Modules for High School
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Upcoming Events and Workshops
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November1
IFML Seminar: 11/1/24 - Tensor Networks and Phase Transitions in Machine Learning
Talk by Cristopher Moore, Resident Professor, Santa Fe Institute
Previously Recorded Talks
New & Noteworthy
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Article
IFML research collaborations power next generation of multimodal AI with OpenCLIP and DataComp
OpenCLIP, the main text/image encoder in Stable Diffusion, is in the top 1% of all Python packages and has more than 50,000 git clones per day. DataComp, is the first rigorous benchmark for advancing multimodal dataset creation.
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Article
Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension earns Best Paper Award at COLT 2024!
Authored by IFML Director Adam Klivans, students Gautam Chandrasekaran, Konstantinos Stavropoulos, IFML postdoc Vasilis Kontonis, and former UT CS PhD Raghu Meka
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Article
Now, Later, and Lasting: 10 Priorities for AI Research, Policy, and Practice
Shaping the future of artificial intelligence
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