KDD 2020: Physics Inspired Models in Artificial Intelligence
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
Intro
Terminology
Motivation: Why Physics & AI?
Why this Tutorial?
Tutorial Goals
Interplay of Physics and Al
The Four Paradigms
Theory vs. Data?
Limitations of the 4th Paradigm
Cautionary Tale: Problems with Big Data
Parameters Galore!
Physics: Tycho Brahe to Kepler to Newton
A Brief History of Physics & Al
Generalization in Physics & Al
Generalization in Neural Nets
Generalization: Observations
Computational Complexity, Al & Physics
Complexity Classes
3-SAT and Phase Transitions
Problems: Complexity
Interpretability & Explainability in Al/ML
Properties of XAI
Physics Informed Neural Nets (PINN)
Physics-guided Neural Network (PGNN)
Physics & Explainable Al: An Illustration
Results Summary
Open Questions in Neural Networks
Statistical physics theory of Deep Learning?
Information Bottleneck & Neural Nets
Information Bottlenecks & Physics
The Committee Machine
Taught by
Association for Computing Machinery (ACM)
Reviews
5.0 rating, based on 1 Class Central review
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Completed viewing course on 26/9/24.
This course helped to introduce these new concepts to me: regularization (methods for correcting model overfitting), 3-SAT problems, Explainable AI (XAI), Physics Informed Neural Networks (PINN), Physics Guided Neural Networks (PGNN), Information bottlenecks in Neural Networks.