6 papers
Discovering Symbolic Differential Equations with Symmetry Invariants
Jianke Yang, Manu Bhat, Bryan Hu +4
Discovering symbolic differential equations from data uncovers fundamental dynamical laws underlying complex systems. However, existing methods often struggle with the vast search…
Symmetry in Neural Network Parameter Spaces
Bo Zhao, Robin Walters, Rose Yu
Modern deep learning models are highly overparameterized, resulting in large sets of parameter configurations that yield the same outputs. A significant portion of this redundancy…
AtlasD: Automatic Local Symmetry Discovery
Manu Bhat, Jonghyun Park, Jianke Yang +3
Existing symmetry discovery methods predominantly focus on global transformations across the entire system or space, but they fail to consider the symmetries in local neighborhoods…
Understanding Mode Connectivity via Parameter Space Symmetry
Bo Zhao, Nima Dehmamy, Robin Walters +1
Neural network minima are often connected by curves along which train and test loss remain nearly constant, a phenomenon known as mode connectivity. While this property has enabled…
Symmetry-Informed Governing Equation Discovery
Jianke Yang, Wang Rao, Nima Dehmamy +2
Despite the advancements in learning governing differential equations from observations of dynamical systems, data-driven methods are often unaware of fundamental physical laws, su…
The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof
Derek Lim, Theo Moe Putterman, Robin Walters +2
Many algorithms and observed phenomena in deep learning appear to be affected by parameter symmetries -- transformations of neural network parameters that do not change the underly…