log in  |  register  |  feedback?  |  help  |  web accessibility
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges (Section 4.2, Grids & Euclidean Spaces)
Lee Sharma - UMD
Wednesday, July 6, 2022, 3:15-4:15 pm Calendar
  • You are subscribed to this talk through .
  • You are watching this talk through .
  • You are subscribed to this talk. (unsubscribe, watch)
  • You are watching this talk. (unwatch, subscribe)
  • You are not subscribed to this talk. (watch, subscribe)

Paper: Geometric Deep Learning Grids, Groups, Graphs, Geodesics, and Gauges by Bronstein et al. (2021) — Chapter 4.2, Grids & Euclidean Spaces

Paper URL: https://arxiv.org/pdf/2104.13478.pdf
Lectures: https://geometricdeeplearning.com/lectures/ (lecture 7)

Paper Abstract: "The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Indeed, many high-dimensional learning tasks previously thought to be beyond reach -- such as computer vision, playing Go, or protein folding -- are in fact feasible with appropriate computational scale. Remarkably, the essence of deep learning is built from two simple algorithmic principles: first, the notion of representation or feature learning, whereby adapted, often hierarchical, features capture the appropriate notion of regularity for each task, and second, learning by local gradient-descent type methods, typically implemented as backpropagation.

While learning generic functions in high dimensions is a cursed estimation problem, most tasks of interest are not generic, and come with essential pre-defined regularities arising from the underlying low-dimensionality and structure of the physical world. This text is concerned with exposing these regularities through unified geometric principles that can be applied throughout a wide spectrum of applications.
Such a 'geometric unification' endeavour, in the spirit of Felix Klein's Erlangen Program, serves a dual purpose: on one hand, it provides a common mathematical framework to study the most successful neural network architectures, such as CNNs, RNNs, GNNs, and Transformers. On the other hand, it gives a constructive procedure to incorporate prior physical knowledge into neural architectures and provide principled way to build future architectures yet to be invented."

For more information and our full schedule, see our website (https://leesharma.com/physics-ai-reading-group/)


This talk is organized by Lee Sharma