Upcoming talks:
Flow-based likelihoods for non-Gaussian inference.
Ana Diaz Rivero, Harvard University, 12:00 EDTEuclidean Neural Networks: Adventures in learning with 3D geometry and geometric tensors.
Tess Smidt, Lawrence Berkeley Laboratory, 12:00 EDTHarnessing Data Revolution in Quantum Matter.
Eun-Ah Kim, Cornell University, 12:00 EDTTBA
Sofia Vallecorsa, CERN, 12:00 EDTTBA
Michelle Ntampaka, Space Telescope Science Institute, 12:00 EDTPast talks:
Machine learning as a discovery tool in hep-th
Vishnu Jejjala, University of Witwatersrand, 12:00 EDTScience is a verb: adopting the scientific method and best practices in AI research
Michela Paganini, Facebook AI Research, 12:00 EDTInsights on gradient-based algorithms in high-dimensional learning
Lenka Zdeborova, EPFL, 12:00 EDTThe large learning rate phase of deep learning
Yasaman Bahri, Google Brain, 12:00 EDTDiscovering new phases of matter with unsupervised and interpretable support vector machines
Lode Pollet, LMU Munich, 12:00 EDTDiscovering Symbolic Models in Physical Systems using Deep Learning
Shirley Ho, Flatiron Institute, 12:00 EDT
For link and password to the talks, please
sign up for the Physics ∩ ML mailing list.
Videos may be found on our YouTube channel and slides by clicking on past talk titles.
Videos may be found on our YouTube channel and slides by clicking on past talk titles.