MotionPyramid learns a stack of latent decoders from motion tracking data to create multi-resolution action interfaces for RL policies in humanoid control, with residual interfaces allowing coarse programs and fine corrections to coexist.
RG-flow: A hierar- chical and explainable flow model based on renormalization group and sparse prior.Machine Learning: Science and Technology, 3(3):035009, August 2022
2 Pith papers cite this work. Polarity classification is still indexing.
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RG-inspired lattice models for piecewise GLMs provide explicit interpretable partitions and a replica-analysis-derived scaling law for regularization that allows increasing complexity without expected rise in generalization loss.
citing papers explorer
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MotionPyramid: Hierarchical Motion Representation and Residual Interfaces
MotionPyramid learns a stack of latent decoders from motion tracking data to create multi-resolution action interfaces for RL policies in humanoid control, with residual interfaces allowing coarse programs and fine corrections to coexist.
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A renormalization-group inspired lattice-based framework for piecewise generalized linear models
RG-inspired lattice models for piecewise GLMs provide explicit interpretable partitions and a replica-analysis-derived scaling law for regularization that allows increasing complexity without expected rise in generalization loss.