GDPD treats partial student features as degraded observations and uses a learned diffusion prior over teacher features to sample restorative long-context targets for improved partial time-series classification.
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arXiv preprint arXiv:2404.07771 , year=
11 Pith papers cite this work. Polarity classification is still indexing.
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The paper establishes an O(ε^{-4}) sample complexity bound for score estimation in diffusion models without requiring access to the empirical risk minimizer.
AdaScope adaptively selects optimal RL intervention points during diffusion denoising by monitoring structural and semantic changes, delivering 66% higher performance at 59% lower cost than full-trajectory RL baselines.
Slowly Annealed Langevin Dynamics provides non-asymptotic KL-based convergence guarantees for tracking moving targets and enables training-free guided generation via a velocity-aware correction that accounts for pretrained marginals.
MMaDA is a unified multimodal diffusion model using mixed chain-of-thought fine-tuning and a new UniGRPO reinforcement learning algorithm that outperforms specialized models in reasoning, understanding, and text-to-image tasks.
NERD uses RL-trained diffusion models on fMRI data to model higher-order uncertainty representations, outperforming controls and linking individual differences to neurofeedback success.
SAS adds semantic scoring with CLIP and a two-stage filter-then-diversity selection process to make generative dataset distillation produce more class-discriminative and diverse compact datasets.
A conditional diffusion model super-resolves coarse ABL LES data, recovering fine turbulent structures and Reynolds stresses accurately inside the training distribution but producing noise and over-predicted stresses when wind speeds are extrapolated.
Establishes robustness of distribution support for guided diffusion processes under exact score access across DDIM, DDPM, and exponential integrator discretizations.
A review of generative AI for inverse design of inorganic compounds, analyzing adaptations for their complexity in composition, geometry, symmetry, and electronic structure, with discussion of future benchmarks and synthesizability metrics.
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Conditional diffusion denoising probabilistic model for super-resolution of atmospheric boundary layer large eddy simulation
A conditional diffusion model super-resolves coarse ABL LES data, recovering fine turbulent structures and Reynolds stresses accurately inside the training distribution but producing noise and over-predicted stresses when wind speeds are extrapolated.