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2021 Machine Learning at the Flatiron Institute Seminar Series

DateSpeakerTitle
January 10, 2021Daniel HsuContrastive learning, multi-view redundancy, and linear models
January 25, 2021Jessica HamrickStructured agents for object-centric reasoning
February 22, 2021Yasaman BahriA Phase Transition in Gradient Descent for Wide, Deep Neural Networks
March 8, 2021Nathan KutzTargeted use of neural networks for scientific discovery
March 22, 2022Yann LeCunnTBD
April 5, 2021Andrew WilsonHow do we build models that learn and generalize?
April 19, 2021Alvaro Sanchez Learning Simulation using Graph Networks
May 3, 2021Stephen BatesDistribution-Free, Risk-Controlling Prediction Sets
June 14, 2021Laura WallerEnd-to-end learning for computational microscopy
July 23, 2021Kimberly StachenfeldRelational Reasoning in Graph Neural Networks and the Hippocampus
August 5, 2021Kyunghyun ChoTrue Few-Shot Learning with Language Models
October 28, 2021Ludovic RighettiRobots robustly interacting with their environment: algorithms and challenges
November 11, 2021Robin WaltersEquivariant Neural Networks for Learning Spatiotemporal Dynamics
November 17, 2021Lenore BlumA Theoretical Computer Science Perspective on Consciousness: Insights from the Conscious Turing Machine
December 2, 2021Christos PapadimitriouHow does the brain beget the mind?
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