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...
Hugging Face Trending Papers
Oct 29
The Precision Wall: An Optimizer-Dependent Boundary for Optimization under Power-Law Measurement Spectra
In low-rank matrix sensing of a rank-$D$ factor $U^\ast \in \mathbb{R}^{V \times D}$, replacing isotropic Gaussian designs with power-law measurement covariance $Σ= \mathrm{diag}(j^{-s})_{j=1}^V$ leaves the Burer--Monteiro landscape free of spurious local minima, yet division-normalised first-order optimizers stall above a critical exponent $s^\ast$. We trace this failure to a numerical precision wall.
arXiv Machine Learning
Sep 30
The Signed Geometry of One-Shot Recourse: On-Path Validity and the Signed-Curvature Criterion
arXiv:2609.36252v1 Announce Type: new Abstract: Closed-form recourse moves a rejected user along the unit gradient $\hat g$ of the classifier score $f$ by the promised distance $d_p=|f(x)|/\|\nabla f...
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.