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Latent Space Symmetry Discovery
Jianke Yang · Nima Dehmamy · Robin Walters · Rose Yu
Event URL: https://openreview.net/forum?id=z3SHey9hK1 »

Existing equivariant neural networks require explicit knowledge of the symmetry group before model implementation. Various symmetry discovery methods have been developed to learn invariance and equivariance from data, but their search spaces are limited to linear symmetries. We propose to discover arbitrary nonlinear symmetries by factorizing the group action into nonlinear transformations parameterized by an autoencoder network and linear symmetries generated by an existing symmetry discovery framework, LieGAN. Our method can capture the intrinsic symmetry in high-dimensional observations, which also results in a well-structured latent space that is useful for other downstream tasks, including long-term prediction and latent space equation discovery.

Author Information

Jianke Yang (University of California, San Diego)
Nima Dehmamy (MIT-IBM Lab)

Physicist working on equivariant neural networks, Symmetries of the loss landscape, graph neural networks, and computational social science.

Robin Walters (Northeastern University)
Rose Yu (University of California, San Diego)
Rose Yu

Dr. Rose Yu is an assistant professor at the University of California San Diego, Department of Computer Science and Engineering. She earned her Ph.D. in Computer Sciences at USC in 2017. She was subsequently a Postdoctoral Fellow at Caltech. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. A particular emphasis of her research is on physics-guided AI which aims to integrate first principles with data-driven models. Among her awards, she has won NSF CAREER Award, Faculty Research Award from JP Morgan, Facebook, Google, Amazon, and Adobe, Several Best Paper Awards, Best Dissertation Award at USC, and was nominated as one of the ’MIT Rising Stars in EECS’.

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