First non-asymptotic sample complexity bounds for structure learning of polynomial exponential families via score matching, with polynomial dependence on model dimension.
International Conference on Learning Representations (ICLR) , year =
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A single diffusion policy network with per-factor null-token dropout enables additive score composition for robot control under conditional independence, with a trajectory-tube certificate, shown to generalize on drone racing tasks.
Non-monotonic sampling schedules never improve upon monotonic baselines in diffusion models, with performance gaps ranging from substantial to negligible depending on the denoiser.
DynamicRad achieves 1.7x-2.5x inference speedups in long video diffusion with over 80% sparsity by grounding adaptive selection in a radial locality prior, using dual-mode static/dynamic strategies and offline BO with a semantic motion router.
citing papers explorer
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Finite Sample Bounds for Learning with Score Matching
First non-asymptotic sample complexity bounds for structure learning of polynomial exponential families via score matching, with polynomial dependence on model dimension.
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Factored Diffusion Policies:Compositionally Generalized Robot Control with a Single Score Network
A single diffusion policy network with per-factor null-token dropout enables additive score composition for robot control under conditional independence, with a trajectory-tube certificate, shown to generalize on drone racing tasks.
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Is Monotonic Sampling Necessary in Diffusion Models?
Non-monotonic sampling schedules never improve upon monotonic baselines in diffusion models, with performance gaps ranging from substantial to negligible depending on the denoiser.
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DynamicRad: Content-Adaptive Sparse Attention for Long Video Diffusion
DynamicRad achieves 1.7x-2.5x inference speedups in long video diffusion with over 80% sparsity by grounding adaptive selection in a radial locality prior, using dual-mode static/dynamic strategies and offline BO with a semantic motion router.