Hugging Face Trending Papers
Matched Queries for Curvature and Density at Branching Junctions
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arXiv Machine Learning
Sep 2
Matched Queries for Curvature and Density at Branching Junctions
arXiv:2609. 01319v1 Announce Type: cross Abstract: At a junction, a score field can reveal weighted tangent rays, yet these first-order quantities do not determine how individual branches bend or how their densities change away from the center.
arXiv Statistics ML
Aug 25
Recovering Weighted Tangent Geometry from a Single-Scale Score Field
arXiv:2608.22334v1 Announce Type: new Abstract: Near a smooth data manifold, one tangent space summarizes local geometry. At a branch point, the corresponding first-order object is instead a measure...
arXiv Machine Learning
Jul 7
Asymptotic-Preserving A Posteriori Analysis of Diffusion and Flow-Matching Samplers
arXiv:2607. 04113v1 Announce Type: new Abstract: Diffusion and flow-matching samplers integrate a learned probability-flow ODE from a large noise scale down to a small terminal floor $\sigma_{\min}$, at which the score is stiff and the flow develops a boundary layer.
arXiv Machine Learning
Aug 17
High-dimensional nonparametric changepoint detection via low-rank degree-two density projection
arXiv:2608. 13922v1 Announce Type: new Abstract: Detecting distributional changes in high dimension is difficult when neither the pre-change nor post-change density is parametrically specified.
Hugging Face Trending Papers
Jul 5
Asymptotic-Preserving A Posteriori Analysis of Diffusion and Flow-Matching Samplers
Diffusion and flow-matching samplers integrate a learned probability-flow ODE from a large noise scale down to a small terminal floor $σ_{\min}$, at which the score is stiff and the flow develops a boundary layer. We treat $σ_{\min}$ as a singular-perturbation parameter and determine which fixed-step samplers are asymptotic-preserving (AP), that is, stable and uniformly accurate as $σ_{\min}\to0$, casting the criteria as an a posteriori audit: residual functionals with $σ_{\min}$-uniform coefficients, computable on a pretrained checkpoint without ground-truth scores or exact trajectories.
arXiv Machine Learning
Jun 25
Onsager-Machlup Posterior Transport for Deep Gaussian Processes
arXiv:2605. 23434v2 Announce Type: replace Abstract: Approximate inference over inducing variables is the central computational bottleneck of Deep Gaussian Processes (DGPs).