Statistical, mathematical, and computational aspects of noisy intermediate-scale quantum computers
Speaker: Gil Kalai (Hebrew University and IDC Herzliya) Title: Statistical, mathematical, and computational aspects of noisy intermediate-scale quantum computers Abstract: Noisy intermediate-scale quantum (NISQ) Computers hold the key for important theoretical and experimental questions regarding […]
Triple Descent and a Fine-Grained Bias-Variance Decomposition
Speaker: Jeffrey Pennington, Google Brain Title: Triple Descent and a Fine-Grained Bias-Variance Decomposition Abstract: Classical learning theory suggests that the optimal generalization performance of a machine learning model should occur […]
Generalization bounds for rational self-supervised learning algorithms, or “Understanding generalizations requires rethinking deep learning”
https://youtu.be/aVB1qFPeEmo Speakers: Boaz Barak and Yamini Bansal, Harvard University Dept. of Computer Science Title: Generalization bounds for rational self-supervised learning algorithms, or "Understanding generalizations requires rethinking deep learning" Abstract: The […]
Some exactly solvable models for machine learning via Statistical physics
https://youtu.be/uUUeTYzMu0Q Speaker: Florent Krzakala, EPFL Title: Some exactly solvable models for machine learning via Statistical physics Abstract: The increasing dimensionality of data in the modern machine learning age presents new […]
Towards AI for mathematical modeling of complex biological systems: Machine-learned model reduction, spatial graph dynamics, and symbolic mathematics
https://youtu.be/t4xRwWxTzSg Speaker: Eric Mjolsness, Departments of Computer Science and Mathematics, UC Irvine Title: Towards AI for mathematical modeling of complex biological systems: Machine-learned model reduction, spatial graph dynamics, and symbolic […]
Re-pricing avalanches
Speaker: Jose A. Scheinkman (Columbia) Title: Re-pricing avalanches Abstract: Monthly aggregate price changes exhibit chronic fluctuations but the aggregate shocks that drive these fluctuations are often elusive. Macroeconomic models often add stochastic macro-level shocks such as […]
Universes as Big Data, or Machine-Learning Mathematical Structures
https://youtu.be/zj_Xc2QG-vw Speaker: Yang-Hui He, Oxford University, City University of London and Nankai University Title: Universes as Big Data, or Machine-Learning Mathematical Structures Abstract: We review how historically the problem of […]
Members’ Seminar
The CMSA Members’ Seminar will occur every Friday at 9:30am ET on Zoom. All CMSA postdocs/members are required to attend the weekly CMSA Members’ Seminars, as well as the weekly CMSA […]
Machine learning and su(3) structures on six manifolds
Speaker: James Gray - Virginia Tech Title: Machine learning and su(3) structures on six manifolds Abstract: In this talk we will discuss the application of Machine Learning techniques to obtain numerical […]