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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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8 results for “diffusion-denoising”

DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Tracesq-bio.BM

DiAMoNDBack models protein backmapping from coarse-grained Cα traces.

problem Restoring all-atom details from coarse-grained protein representations.

method Autoregressive denoising diffusion model for residue-by-residue backmapping.

result Achieves state-of-the-art reconstruction performance in diverse applications.

protein backmapping coarse-grained models autoregressive models diffusion-denoising all-atom details

2023-07-23abs ↗pdf ↗

Diffusion Denoising Process for Perceptron Bias in Out-of-distribution Detectioncs.CV

New method improves OOD detection by integrating diffusion models into discriminator models.

problem Overconfidence in discriminator models leads to poor OOD detection.

method Integrates diffusion models into discriminator and generation models to mitigate overconfidence.

result Demonstrates significant improvement in AUROC scores for challenging datasets.

OOD detection discriminator models overconfidence diffusion models image classification

2022-11-21abs ↗pdf ↗

Micro-Diffusion Compression - Binary Tree Tweedie Denoising for Online Probability Estimationstat.ML

Midicoth compresses online probability estimates by correcting prior smoothing biases.

problem Compression inefficiency due to prior smoothing in adaptive models.

method Micro-diffusion denoising applied in a bitwise tree hierarchy.

result Significant compression improvement with reliable calibration.

compression probability estimation denoising online learning adaptive models

2026-03-09abs ↗pdf ↗

Smoothing the Landscape: Causal Structure Learning via Diffusion Denoising Objectivescs.LG

DDCD uses diffusion models to learn causal structures from noisy data.

problem Scalability and stability issues in high-dimensional causal structure learning.

method Adaptive k-hop acyclicity constraint and denoising score matching objective of diffusion models.

result DDCD achieves competitive performance on synthetic and real-world data.

causal discovery diffusion models denoising graphical models scalability

2026-04-02abs ↗pdf ↗

CADO: From Imitation to Cost Minimization for Heatmap-based Solvers in Combinatorial Optimizationcs.LG

CADO optimizes heatmap-based solvers for cost minimization, overcoming performance limitations.

problem Heatmap-based solvers lack objective alignment for cost minimization.

method CADO uses Reinforcement Learning to optimize solution cost directly, introducing Label-Centered Reward and Hybrid Fine-Tuning.

result CADO achieves state-of-the-art performance across diverse benchmarks.

Reinforcement Learning Diffusion Models Cost Minimization Heatmap Solvers Combinatorial Optimization

2026-02-09abs ↗pdf ↗

ScoreFusion: Fusing Score-based Generative Models via Kullback-Leibler Barycentersstat.ML

ScoreFusion fuses multiple diffusion models to enhance generative modeling of a target population.

problem Enhancing generative modeling of a target population with limited data.

method ScoreFusion uses KL barycenters of auxiliary populations and recasts the learning problem as score matching in denoising diffusion.

result ScoreFusion achieves a dimension-free sample complexity bound in total variation distance.

ScoreFusion KL barycenter denoising diffusion generative modeling target population

2024-06-28abs ↗pdf ↗

Multi-scale Diffusion Denoised Smoothingcs.LG

New method improves robustness of large models without sacrificing accuracy.

problem Improving robustness of large pre-trained models without accuracy loss.

method Multi-scale diffusion denoised smoothing, selectively applying smoothing at multiple noise scales.

result Strong certified robustness at high noise levels with accuracy close to non-smoothed classifiers.

diffusion models randomized smoothing adversarial robustness large models denoising

2023-10-25abs ↗pdf ↗

Score-Based Generative Modeling with Critically-Damped Langevin Diffusionstat.ML

Improved generative models using critically-damped Langevin diffusion.

problem Current score-based generative models (SGMs) use overly simplistic diffusion processes, leading to complex denoising tasks and suboptimal performance.

method Proposed a novel critically-damped Langevin diffusion (CLD) and derived a score matching objective and sampling scheme.

result CLD-based SGMs achieve superior performance in synthesis quality compared to previous methods.

score-based generative models diffusion processes critically-damped Langevin diffusion denoising statistical mechanics

2021-12-14abs ↗pdf ↗