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Found 2 papers, 1 papers with code
Geometry-Informed Neural Networks
We introduce the concept of geometry-informed neural networks (GINNs), which encompass (i) learning under geometric constraints, (ii) neural fields as a suitable representation, and (iii) generating diverse solutions to under-determined systems often encountered in geometric tasks.
A Dataset Perspective on Offline Reinforcement Learning
The dataset characteristics are determined by the behavioral policy that samples this dataset.
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