Symplectic duality in examples

Science Center Hall E 1 Oxford Street, Cambridge, MA, United States

Geometry and Quantum Theory Seminar Speaker: Vasily Krylov, Harvard CMSA & Math Title: Symplectic duality in examples Abstract: Over the past twenty years, mathematicians and physicists have shown increasing interest […]

Infinite Limits and Scaling Laws for Deep Neural Networks

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

https://youtu.be/0998FJhPdj8 New Technologies in Mathematics Seminar Speaker: Blake Bordelon Title: Infinite Limits and Scaling Laws for Deep Neural Networks Abstract: Scaling up the size and training horizon of deep learning models has enabled […]

Witten deformation for non-Morse functions and gluing formulas 

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

Mathematical Physics and Algebraic Geometry Speaker: Junrong Yan (Northeastern University) Title: Witten deformation for non-Morse functions and gluing formulas Abstract: Witten deformation is a versatile tool with numerous applications in […]

Topics in Deep Learning Theory

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

Topics in Deep Learning Theory Eli Grigsby

Going to the other side …. in algebra, topology, and maybe physics

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

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 […]

Quasinormal Corrections to Near-Extremal Black Hole Thermodynamics

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

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 […]

Topics in Deep Learning Theory

CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

Topics in Deep Learning Theory Eli Grigsby