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Fri Dec 11 05:45 AM -- 02:00 PM (PST)
Differential Geometry meets Deep Learning (DiffGeo4DL)
Joey Bose · Emile Mathieu · Charline Le Lan · Ines Chami · Frederic Sala · Christopher De Sa · Maximilian Nickel · Christopher Ré · Will Hamilton

Workshop Home Page

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. (Social)
Opening Remarks (Presentation)
Invited Talk 1: Geometric deep learning for 3D human body synthesis (Talk by Michael Bronstein)
Invited Talk 2: Gauge Theory in Geometric Deep Learning (Talk by Taco Cohen)
Contributed Talk 1: Learning Hyperbolic Representations for Unsupervised 3D Segmentation (Contributed Talk)
Contributed Talk 2: Witness Autoencoder: Shaping the Latent Space with Witness Complexes (Contributed Talk 2)
Contributed Talk 3: A Riemannian gradient flow perspective on learning deep linear neural networks (Contributed Talk 3)
Contributed Talk 4: Directional Graph Networks (Contributed Talk 4)
Contributed Talk 5: A New Neural Network Architecture Invariant to the Action of Symmetry Subgroups (Contributed Talk 5)
Virtual Coffee Break on Gather.Town (Break)
Invited Talk 3: Reparametrization invariance in representation learning (Talk by Søren Hauberg)
Poster Session 1 on Gather.Town (Poster Session)
Tree Covers: An Alternative to Metric Embeddings (Poster)
Grassmann Iterative Linear Discriminant Analysis with Proxy Matrix Optimization (Poster)
Quaternion Graph Neural Networks (Poster)
Hermitian Symmetric Spaces for Graph Embeddings (Poster)
Deep Networks and the Multiple Manifold Problem (Poster)
Isometric Gaussian Process Latent Variable Model (Poster)
GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability (Poster)
A Metric for Linear Symmetry-Based Disentanglement (Poster)
Universal Approximation Property of Neural Ordinary Differential Equations (Poster)
Graph of Thrones : Adversarial Perturbations dismantle Aristocracy in Graphs (Poster)
Panel Discussion (Panel)
Virtual Coffee Break on Gather.Town (Break)
Focused Breakout Session Companion Notebook: Wrapped Normal Distribution (Demonstration)
Focused Breakout Session Companion Notebook: Poincare Embeddings (Demonstration)
Focused Breakout Session (Demonstration)
Invited Talk 4: An introduction to the Calderon and Steklov inverse problems on Riemannian manifolds with boundary (Talk by Niky Kamran)
QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings (Poster)
Unsupervised Orientation Learning Using Autoencoders (Poster)
Poster Session 2 on Gather.Town (Poster Session)
Leveraging Smooth Manifolds for Lexical Semantic Change Detection across Corpora (Poster)
Convex Optimization for Blind Source Separation on a Statistical Manifold (Poster)
Sparsifying networks by traversing Geodesics (Poster)
The Intrinsic Dimension of Images and Its Impact on Learning (Poster)
Towards Geometric Understanding of Low-Rank Approximation (Poster)
Extendable and invertible manifold learning with geometry regularized autoencoders (Poster)
Deep Riemannian Manifold Learning (Poster)
Affinity guided Geometric Semi-Supervised Metric Learning (Poster)
Invited Talk 5: Disentangling Orientation and Camera Parameters from Cryo-Electron Microscopy Images Using Differential Geometry and Variational Autoencoders (Talk by Nina Miolane)
Invited Talk 6: Learning a robust classifier in hyperbolic space (Talk by Melanie Weber)