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
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 […]
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 […]
Mathematical Physics and Algebraic Geometry Seminar Speaker: Chuck Doran, Harvard CMSA Title: Enumerative geometry and modularity in two-modulus K3-fibered Calabi-Yau threefolds Abstract: Smooth $M_m$-polarized K3-fibered Calabi-Yau (CY) 3-folds have been […]
Quantum Field Theory and Physical Mathematics Seminar Speaker: Giulia Fardelli, Boston University Title: Holography and Regge Phases at Large U(1) Charge Abstract: A single Conformal Field Theory (CFT) can have […]
Math and Machine Learning Program Discussion
Member Seminar Speaker: Hugo Cui, CMSA Title: High-dimensional learning of narrow neural networks Abstract: This talk explores the interplay between neural network architectures and data structure through the lens of high-dimensional asymptotics. […]
Math and Machine Learning Program Discussion
Colloquium Speaker: Elisenda Grigsby, Boston College Title: Local complexity measures in modern parameterized function classes for supervised learning Abstract: The parameter space for any fixed architecture of neural networks serves as […]