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Differential Geometry meets Deep Learning (DiffGeo4DL)
Joey Bose · Emile Mathieu · Charline Le Lan · Ines Chami · Fred Sala · Christopher De Sa · Maximilian Nickel · Chris Ré · Will Hamilton

Fri Dec 11 05:45 AM -- 02:00 PM (PST) @ None
Event URL: https://sites.google.com/view/diffgeo4dl/ »

Recent years have seen a surge in research at the intersection of differential geometry and deep learning, including techniques for stochastic optimization on curved spaces (e.g., hyperbolic or spherical manifolds), learning embeddings for non-Euclidean data, and generative modeling on Riemannian manifolds. Insights from differential geometry have led to new state of the art approaches to modeling complex real world data, such as graphs with hierarchical structure, 3D medical data, and meshes.
Thus, it is of critical importance to understand, from a geometric lens, the natural invariances, equivariances, and symmetries that reside within data.

In order to support the burgeoning interest of differential geometry in deep learning, the primary goal for this workshop is to facilitate community building and to work towards the identification of key challenges in comparison with regular deep learning, along with techniques to overcome these challenges. With many new researchers beginning projects in this area, we hope to bring them together to consolidate this fast-growing area into a healthy and vibrant subfield. In particular, we aim to strongly promote novel and exciting applications of differential geometry for deep learning with an emphasis on bridging theory to practice which is reflected in our choices of invited speakers, which include both machine learning practitioners and researchers who are primarily geometers.

Author Information

Joey Bose (McGill/MILA)

I’m a PhD student at the RLLab at McGill/MILA where I work on Adversarial Machine Learning on Graphs. Previously, I was a Master’s student at the University of Toronto where I researched crafting Adversarial Attacks on Computer Vision models using GAN’s. I also interned at Borealis AI where I was working on applying adversarial learning principles to learn better embeddings i.e. Word Embeddings for Machine Learning models.

Emile Mathieu (University of Oxford)
Charline Le Lan (University of Oxford)
Ines Chami (Stanford University)
Fred Sala (U. Wisconsin-Madison)
Christopher De Sa (Cornell)
Maximilian Nickel (Facebook)
Chris Ré (Stanford)
Will Hamilton (McGill)

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