arXiv AI
Denoising as Projection: Constrained Optimization with Gradient-Guided Diffusion
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 17
Integration Matters: Rollout-Based Training for Constrained Diffusion Models
arXiv:2607. 14398v1 Announce Type: cross Abstract: Constrained generative models aim to produce samples that satisfy complex feasibility constraints while remaining faithful to the data distribution.
arXiv Machine Learning
Jun 16
DiRecT: Safe Diffusion-Based Planning via Receding-Horizon Denoising
arXiv:2606. 15359v1 Announce Type: new Abstract: Diffusion models have emerged as powerful tools for planning and control by learning multimodal distributions over actions and trajectories.
arXiv Machine Learning
Aug 31
Diffusion models as plug-and-play priors
The paper explores using denoising diffusion generative models as plug‑and‑play priors for high‑dimensional inference problems. By combining a pre‑trained diffusion prior with a differentiable auxiliary constraint, the authors enable approximate inference through iterative differentiation across multiple noisy versions of the data. This framework opens possibilities for conditional generation, image segmentation, and novel combinatorial optimization algorithms.
arXiv Machine Learning
Jun 2
Efficient Diffusion Models under Nonconvex Equality and Inequality constraints via Landing
arXiv:2604. 17838v2 Announce Type: replace Abstract: Generative modeling within constrained sets is essential for scientific and engineering applications involving physical, geometric, or safety requirements (e.
arXiv Machine Learning
Sep 14
Deep learning methods for inverse problems using connections between proximal operators and Hamilton-Jacobi equations
The paper proposes a deep learning framework that learns priors for inverse problems by exploiting the relationship between proximal operators and Hamilton–Jacobi partial differential equations. Unlike existing methods that require inverting the prior after training, this approach learns the prior directly, enabling efficient evaluation in a single forward pass. Numerical experiments demonstrate the method’s effectiveness in dimensions up to 64.
arXiv Machine Learning
Jul 7
Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models
arXiv:2607. 04775v1 Announce Type: cross Abstract: Score-based Generative Models (SGMs) have achieved impressive performance in data generation across a wide range of applications.