Geometric Deep Learning Study Group

We’re working on our way through “Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges” by Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković We’ll use the chapters rather than the big pdf, and also presenting on other papers.

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Session 1: August 11, at 11am EDT/3pm GMT/5pm CEDT

Slides

Session 2: September 1, at 11am EDT/3pm GMT/5pm CEDT

Reading: Introduction

Lecture: ICLR 2021 Keynote: GDL: The Erlangen Programme of ML by M Bronstein”

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Additional References: Mathematical Foundations of Geometric Deep Learning by Borde and Bronstein Introduction to Geometric Deep Learning by Patrick Nicolas

Presentation Topic: Geometric Deep Learning Reveals the Spatio-Temporal Features of Microscopic Motion Aaron Presenting

Session 3: September 22, at 11am EDT/3pm GMT/5pm CEDT

Reading: Graphs

Lecture: Graph Neural Networks with Petar Velickovic

Tutorial: Intro to GNNs

Optional Lectures on GNNs: Deep learning on graphs: successes, challenges by Bronstein

Graph Convolutional Networks by Federico Barbero

Graph Attention Networks by Federico Barbero

Presentations:

Working through “Intro to GNNs” Tutorial

Possible Project on TacticAI

Session 4: October 13, at 11am EDT/3pm GMT/5pm CEDT

Lecture: Graph Neural Networks for Geometric Graphs by Chaitanya Joshi

Tutorial: Molecular Property Prediction with GNNs

Presentations: Working through “Molecular Property Prediction with GNNs” tutorial

Session 5: November 3, at 11am EDT/3pm GMT/5pm CEDT

Readings: Foundations of Supervised Learning

Foundations of Equivariant Learning

Lecture: Equivariant Networks by Taco Cohen

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Possible Presentation Topics: Valence Labs Graph Learning on Wednesdays

Previous projects:

• Privacy-preserving Detection of Violence in Cameras Writing a privacy product specification for a privacy-preserving neural network for video analysis as part of my capstone for the OpenMined course, “Our Privacy Opportunity.” Privacy-Preserving Violence Detector

• Mean Shift Analysis of the Uniform Crime Reporting data published by the FBI. I use TensorFlow 2.0 to do the analysis and bokeh to create the map.
  • UCRanalysis.ipnyb
  • UCRanalysis.html
  • crime_us_map.html

• Better Bin Packing Through Matrix Multiplication Using matrix multiplication to speed up a classic optimization problem

  Latest Version

Excerpt of Metamorphosis II, by Marc Chagall

• Optimizing Sailor Health for NavalX’s Hack The Machine. Part of second place finish in Data Science Track. Wrote code for Challenge 3: Optimizing Sailor Health

• Pruned Neural Networks for Melanoma Detection Competing in Kaggle’s Melanoma Detection Competition through application of the Lottery Ticket Hypothesis for neural network pruning. Secondary goal of building more explainable artificial intelligence. Repo here

• Quantum Computing for Network Analysis A proof-of-concept for identifying likely asymptomatic CoVID-19 carriers through network analysis by applying simulated quantum computing with qiskit. Entry into Qiskit Community Summer Jam 2020 https://qiskit-community-summer-jam-new-england.hackerearth.com/

  Final Presentation

End of Metamorphosis II, by Marc Chagall

Future plans:

• AI4EO Air Quality and Health Challenge

• Recursively defined sparse matrices A new way to build, add, and multiply sparse matrixes for graph neural networks and pruning