Distinguishing Monotone Lagrangians via Holomorphic Annuli - Ailsa Keating
Explore advanced symplectic geometry concepts, focusing on distinguishing monotone Lagrangians through holomorphic annuli, with insights on geometric mutation and moduli spaces.
Independence of ℓ for Frobenius Conjugacy Classes Attached to Abelian Varieties - Rong Zhou
Explore Frobenius conjugacy classes in abelian varieties, focusing on independence of ℓ, Mumford-Tate groups, and connections to Shimura varieties and CM liftings.
Floer Cohomology and Arc Spaces - Mark McLean
Explore Floer cohomology, arc spaces, and symplectic geometry with Mark McLean. Delve into contact structures, jet spaces, and mapping moduli spaces in this advanced mathematical lecture.
Mirrors of Curves and Their Fukaya Categories - Denis Auroux
Explore symplectic geometry with Denis Auroux as he delves into mirrors of curves and their Fukaya categories, covering topics from monomial examples to Lagrangian admissibility.
Neural SDEs - Deep Generative Models in the Diffusion Limit - Maxim Raginsky
Explore deep generative models through stochastic control, diffusion limits, and neural nets. Learn about efficient sampling, expressiveness quantification, and unbiased simulation techniques.
Reflections on Cylindrical Contact Homology - Jo Nelson
Explore cylindrical contact homology, its evolution, and recent developments in symplectic geometry with Jo Nelson's in-depth seminar on periodic orbits and obstruction bundles.
A Geometric View on Iwasawa Theory - Mladen Dimitrov
Explore a geometric perspective on Iwasawa theory, covering higher dimensions, periodic representations, and key concepts in number theory with Mladen Dimitrov from Université de Lille.
Generative Modeling by Estimating Gradients of the Data Distribution - Stefano Ermon
Explore generative modeling through gradient estimation of data distributions, covering deep energy-based models, score matching, and innovative sampling techniques for improved AI generation.
Mathematical Models of Human Memory - Michail Tsodyks
Explore mathematical models of human memory, covering simplification, recall capacity, experiments, and recognition tests, with insights on memory dynamics and cognitive processes.
High Dimensional Expansion and Error Correcting Codes - Irit Dinur
Explore high-dimensional expansion, error-correcting codes, and their connections. Delve into expansion constructions, property testing, and revolution complexes with expert Irit Dinur.
Extreme Eigenvalue Distributions of Sparse Random Graphs - Jiaoyang Huang
Explore extreme eigenvalue distributions in sparse random graphs, covering empirical distributions, edge expansion, routing graphs, and universal properties. Gain insights into mathematical physics and graph theory.
Lie Algebras and Homotopy Theory - Jacob Lurie
Explore connections between Lie algebras and homotopy theory, covering fundamental groups, Whitehead brackets, and Quillen's theorem in this advanced mathematics seminar.
Towards Structural Risk Minimization for RL - Emma Brunskill
Explore structural risk minimization in reinforcement learning, focusing on efficient learning of risk-sensitive policies using conditional value at risk and optimistic exploration techniques.
Pseudoholomorphic Curves with Boundary - Can You Count Them? Can You Really? - Sara Tukachinsky
Explore pseudoholomorphic curves with boundary, their counting challenges, and innovative approaches in Gromov-Witten theory with Sara Tukachinsky's advanced mathematical seminar.
Extremal Set Theory - Andrey Kupavskii
Explore extremal set theory with Andrey Kupavskii, covering shifting techniques, partial orders, and the Katniss Theorem. Gain insights into minimizing shadows and analyzing complex cases in discrete mathematics.
Sparse Matrices in Sparse Analysis - Anna Gilbert
Explore sparse matrices in compressive sensing, focusing on expander graphs and their applications. Learn about metric repair scenarios and algorithms for sparse signal recovery.
Primes and Knots - Akshay Venkatesh
Explore surprising parallels between number theory and 3D geometry, uncovering connections that continue to shape mathematical research.
Optimization Landscape and Two-Layer Neural Networks - Rong Ge
Explore optimization landscapes and two-layer neural networks, focusing on non-convexity, saddle points, and symmetric distributions in theoretical machine learning.
Learning Representations Using Causal Invariance - Leon Bottou
Explore causal invariance in machine learning representations, focusing on stable properties, extrapolation, and robust supervised learning techniques for improved model performance and generalization.
From Classical Statistics to Modern ML - The Lessons of Deep Learning - Mikhail Belkin
Explore the transition from classical statistics to modern machine learning, focusing on deep learning's impact, interpolation methods, and the "double descent" phenomenon in generalization theory.
Energy-Based Approaches to Representation Learning - Yann LeCun
Explore energy-based approaches to representation learning with Yann LeCun, covering self-supervised learning, video prediction, and sparse modeling in high-dimensional spaces.