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.
citing papers explorer
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Generative Diffusion Prior Distillation for Long-Context Knowledge Transfer
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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Improved Sample Complexity For Diffusion Model Training Without Empirical Risk Minimizer Access
The paper establishes an O(ε^{-4}) sample complexity bound for score estimation in diffusion models without requiring access to the empirical risk minimizer.
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Do Less, Achieve More: Do We Need Every-Step Optimization for RL Fine-tuning of Diffusion Models?
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.
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Slowly Annealed Langevin Dynamics: Theory and Applications to Training-Free Guided Generation
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.
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MMaDA: Multimodal Large Diffusion Language Models
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.
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Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance
NERD uses RL-trained diffusion models on fMRI data to model higher-order uncertainty representations, outperforming controls and linking individual differences to neurofeedback success.
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SAS: Semantic-aware Sampling for Generative Dataset Distillation
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.
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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.
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On the Robustness of Distribution Support under Diffusion Guidance
Establishes robustness of distribution support for guided diffusion processes under exact score access across DDIM, DDPM, and exponential integrator discretizations.
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Inverse Design of Inorganic Compounds with Generative AI
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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