Topics in Deep Learning Theory
CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United StatesTopics in Deep Learning Theory Eli Grigsby
Topics in Deep Learning Theory Eli Grigsby
Quantum Field Theory and Physical Mathematics Speaker: Sergei Gukov (Caltech) Title: Going to the other side .... in algebra, topology, and maybe physics Abstract: Inspired by Eugene Wigner's reflections on the 'unreasonable effectiveness of mathematics in the natural sciences,' this talk is about the surprising and pervasive role of a peculiar phenomenon that, a priori, […]
Math and Machine Learning Program Discussion
Math and Machine Learning Program Discussion
https://youtu.be/Pht8aXE5IsU Machine Learning in Science Education Panel Discussion Monday, Sep. 30, 2024 3:30-5:30 pm ET Machine Learning is rapidly influencing many spheres of human activity. As part of the CMSA Mathematics and Machine Learning Program, this panel discussion will explore current and future uses of Machine Learning in science education. Panelists will make brief presentations, […]
General Relativity Seminar Speaker: Daniel Kapec, Harvard Title: Quasinormal Corrections to Near-Extremal Black Hole Thermodynamics Abstract: Recent work on the quantum mechanics of near-extremal non-supersymmetric black holes has identified a characteristic scaling of the low temperature black hole partition function. This result has only been derived using the path integral in the near-horizon region and relies […]
Topics in Deep Learning Theory Eli Grigsby
Open Discussion/Tea
Geometry and Quantum Theory Seminar Speaker: Bowen Yang, Harvard CMSA Title: Topological Invariants of gapped states through cosheaves Abstract: We provide a proper mathematical framework for the constructions of topological invariants of gapped quantum states and interpret topological invariants of gapped states as lattice analogs of ’t Hooft anomalies in Quantum Field Theory. Our secondary […]
Math and Machine Learning Program Discussion
CMSA Q&A Seminar Speaker: Cliff Taubes, Harvard Mathematics Topic: What are Z/2 harmonic 1-forms?
https://youtu.be/x7LPDDYZn94 New Technologies in Mathematics Seminar Speaker: Antonio Sclocchi, EPFL Title: Hierarchical data structures through the lenses of diffusion models Abstract: The success of deep learning with high-dimensional data relies on the fact that natural data are highly structured. A key aspect of this structure is hierarchical compositionality, yet quantifying it remains a challenge. In […]