Topics in Deep Learning Theory
Topics in Deep Learning Theory Eli Grigsby
Topics in Deep Learning Theory Eli Grigsby
Geometry and Quantum Theory Seminar Speaker: Mayuko Yamashita, Kyoto University Title: Topological Modular Forms, its equivariant refinements and relation with supersymmetric quantum field theories Abstract: This talk is about the […]
Math and Machine Learning Program Discussion
CMSA Q&A Seminar Speaker: Nazim Bouatta (HMS) Topic: What are AlphaFold2 and OpenFold
https://youtu.be/OYOuSAAE7QQ New Technologies in Mathematics Seminar Speaker: Anirudh Goyal (University of Montreal) Title: From Word Prediction to Complex Skills: Data Flywheels for Mathematical Reasoning Abstract: This talk examines how large language models […]
Speaker: Andrew Neitzke, Yale University Location: Harvard University Science Center Hall D & via Zoom webinar Dates: October 16 & 17, 2024 Time: 4:00 pm https://youtu.be/1SyGyeMmWoM Wednesday, Oct. […]
Topics in Deep Learning Theory Eli Grigsby
Speaker: Andrew Neitzke, Yale University Location: Harvard University Science Center Hall D & via Zoom webinar Dates: October 16 & 17, 2024 Time: 4:00 pm https://youtu.be/1SyGyeMmWoM Wednesday, Oct. 16, […]
Quantum Field Theory and Physical Mathematics Seminar *via Zoom only* Speaker: Rajath Radhakrishnan (ICTP, Trieste) Title: Bosonic and fermionic 1-form symmetries and anomaly matching Abstract: In this talk, I will consider […]
Math and Machine Learning Program Discussion
Member Seminar Speaker: Puskar Mondal Title: Positive mass and rigidity theorems in Riemannian geometry Abstract: Positive mass theorem proved by Schoen-Yau, Witten, Taubes-Parker is one of the most important results in […]
Foundation Seminar (Joint Seminar with BHI) Location: BHI Title: Singularity Theorems, Part I Journal Club Discussion