arXiv Machine Learning
Parallel Tempering for Diffusion-Based Combinatorial Optimization
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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
CADO: From Imitation to Cost Minimization for Heatmap-based Solvers in Combinatorial Optimization
arXiv:2602. 08210v2 Announce Type: replace Abstract: Heatmap-based solvers have emerged as a promising paradigm for Combinatorial Optimization (CO).
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
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 Computer Vision
2d ago
Principled Design of Diffusion-based Optimizers for Inverse Problems
arXiv:2605.11506v2 Announce Type: replace Abstract: Score-based diffusion models achieve state-of-the-art performance for inverse problems, but their practical deployment is hindered by long inferenc...
arXiv Machine Learning
Sep 22
D-IMPL: A Diffusion-based Solver for Parameterized BBOs
arXiv:2609. 22752v1 Announce Type: new Abstract: Diffusion models have demonstrated strong power in generative modeling tasks across multiple domains, exhibiting a remarkable capability of learning complex distributions from samples.