score-based-models

A framework combining deep generative models (flow and score-based) with symbolic regression to automatically infer analytical forms of unnormalized probability densities from samples, demonstrated on toy distributions and lattice physics systems like the XY model and φ⁴ theory renormalization.

The paper introduces a specificity-aware diffusion steering method that designs an explicit target distribution from an overlap-based objective and samples it with a variance-reduced Sequential Monte Carlo sampler, improving negative-guidance effectiveness and sampling stability across image generation and peptide-MHC binder tasks.

The paper proposes EnJoi, an ensemble joint score filter for generative data assimilation that learns the joint distribution of past and future states, enabling a modified En4DVar approach to balance confidence in the current state versus new observations. Experiments on fluid and traffic flow simulations show improved reconstruction, especially with sparse, non-homogeneous observations.

This paper introduces a continuous-time generative dynamics framework that leverages score-based models to generate data at arbitrary timestamps on learned data manifolds, with applications to video and scientific data.

This paper provides a proof-oriented introduction to diffusion models, covering Langevin dynamics, score-based models, discretization, discrete diffusion, and inference-time control, intended for graduate students.

This paper proposes a class-frequency guided noise schedule for diffusion models that assigns larger-scale noises to low-frequency classes to improve generation quality on imbalanced datasets, demonstrating substantial improvements over baselines.

This paper proposes primal-dual inference for constrained diffusion models, jointly inferring the optimal distribution and its dual variable via a dual-conditioned score network, with convergence guarantees and applications in wireless resource allocation and portfolio management.

This arXiv preprint proposes a unified measure-theoretic framework for understanding diffusion, score-based, and flow matching generative models. It establishes connections between these methods via continuity/Fokker-Planck equations and analyzes their sampling schemes and theoretical guarantees.