9 citations · 18 across the 9 of their papers we have counts for
8 papers · 1 filter
Approximate Equivariance in Reinforcement Learning
Jung Yeon Park, Sujay Bhatt, Sihan Zeng +4
Equivariant neural networks have shown great success in reinforcement learning, improving sample efficiency and generalization when there is symmetry in the task. However, in many…
Modeling Dynamics over Meshes with Gauge Equivariant Nonlinear Message Passing
Jung Yeon Park, Lawson L. S. Wong, Robin Walters
Data over non-Euclidean manifolds, often discretized as surface meshes, naturally arise in computer graphics and biological and physical systems. In particular, solutions to partia…
Can Euclidean Symmetry be Leveraged in Reinforcement Learning and Planning?
Linfeng Zhao, Owen Howell, Jung Yeon Park +3
In robotic tasks, changes in reference frames typically do not influence the underlying physical properties of the system, which has been known as invariance of physical laws.These…
A General Theory of Correct, Incorrect, and Extrinsic Equivariance
Dian Wang, Xupeng Zhu, Jung Yeon Park +4
Although equivariant machine learning has proven effective at many tasks, success depends heavily on the assumption that the ground truth function is symmetric over the entire doma…
The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry
Dian Wang, Jung Yeon Park, Neel Sortur +3
Extensive work has demonstrated that equivariant neural networks can significantly improve sample efficiency and generalization by enforcing an inductive bias in the network archit…
Robust Imitation of a Few Demonstrations with a Backwards Model
Jung Yeon Park, Lawson L. S. Wong
Behavior cloning of expert demonstrations can speed up learning optimal policies in a more sample-efficient way over reinforcement learning. However, the policy cannot extrapolate…
Learning Symmetric Embeddings for Equivariant World Models
Jung Yeon Park, Ondrej Biza, Linfeng Zhao +2
Incorporating symmetries can lead to highly data-efficient and generalizable models by defining equivalence classes of data samples related by transformations. However, characteriz…
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng +2
Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports, and climate science. Tensor latent factor models can describe higher-order correlati…