denoising score matching
PulseAugur coverage of denoising score matching — every cluster mentioning denoising score matching across labs, papers, and developer communities, ranked by signal.
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New research explores diffusion score estimation with shallow neural networks
Researchers have developed a method for estimating diffusion scores using shallow neural networks, specifically focusing on shallow ReLU networks. The study provides theoretical bounds on the sample complexity and appro…
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New Uni-DSM framework unifies video anomaly detection and anticipation
Researchers have introduced Uni-DSM, a novel framework that unifies video anomaly detection and anticipation using a score-matching paradigm. This approach models anomalies through likelihood estimation and score functi…
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Uni-DSM framework unifies video anomaly detection and anticipation
Researchers have introduced Uni-DSM, a novel unified framework for video anomaly detection and anticipation based on denoising score matching. This approach models anomalies by estimating likelihoods and score functions…
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Score Matching Linked to ML and EM in Mixed Linear Regression
Researchers have established a theoretical connection between score matching, maximum likelihood estimation, and the expectation-maximization (EM) algorithm within the context of mixed linear regression models. Their an…
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New research links diffusion model training loss to Fisher geometry
Researchers have analyzed the irreducible excess loss in denoising score matching for diffusion models. They found this excess loss is directly related to the Fisher--Rao metric of the conditional endpoint family, integ…
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New STEP framework improves human pose video anomaly detection
Researchers have developed a new framework called STEP (Score-Based Temporal Energy) for detecting anomalies in human pose videos. This method addresses a key challenge in existing approaches by using Principal Componen…
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New research reveals inherent instability in Denoising Score Matching
A new arXiv paper by Juyan Zhang and colleagues explores the inherent heteroscedasticity in Denoising Score Matching (DSM), a technique widely used in generative models. The researchers demonstrate that DSM's objective …
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Neuroscience-inspired diffusion model explains visual cortex inference
Researchers have developed a novel model that bridges neuroscience and machine learning by explaining perceptual inference in the primary visual cortex (V1) through the lens of diffusion models. This model, based on spa…
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Paper analyzes SGD convergence for score-based generative models
Researchers have published a paper analyzing the optimization dynamics of training Score-based Generative Models (SGMs). The study focuses on Stochastic Gradient Descent (SGD) and provides convergence rates for general …
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New research details SGD convergence for score-based generative models
Researchers have published a paper detailing the non-asymptotic convergence of Stochastic Gradient Descent (SGD) when applied to Score-based Generative Models (SGMs). The study provides theoretical guarantees for SGD in…
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Diffusion Models: Theory Explains Generalization and Memorization
Researchers have developed a theoretical framework to understand generalization and memorization in diffusion models. Their work derives precise expressions for test and train errors in Denoising Score Matching (DSM) us…
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The Feedback Hamiltonian is the Score Function: A Diffusion-Model Framework for Quantum Trajectory Reversal
Researchers have established a theoretical link between quantum trajectory reversal and score-based diffusion models used in machine learning. They demonstrated that the feedback Hamiltonian, which can statistically rev…