arXiv Machine Learning
FlashDiffusion: Fused Tiled Kernel Spectral Decomposition
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arXiv Machine Learning
Sep 1
Spectral Analysis for Sparse Matrix Computation: Insights and Potential
arXiv:2608.29362v1 Announce Type: cross Abstract: Sparse computations are fundamental to scientific computing, graph analytics, and machine learning, yet their performance is highly sensitive to the...
arXiv Machine Learning
Sep 4
Geometry-Aware Graph Construction via Adaptive Spectral Bandwidth Control
The paper introduces a geometry‑aware graph construction method that adaptively selects Gaussian kernel bandwidths per node to align the kernel’s spectral complexity with the intrinsic dimensionality of the underlying manifold. By matching the kernel’s effective rank to a local intrinsic dimension estimate derived from a minimum spanning tree, the method operates within a manifold‑consistent log‑log scaling regime. Experiments on CIFAR‑100 demonstrate that this adaptive bandwidth approach consistently improves leave‑one‑out classification and label propagation accuracy compared to fixed‑bandwidth and other adaptive techniques.
arXiv Machine Learning
Aug 11
Multi-kernel spectral clustering: Entrywise eigenvector perturbation bounds and exact recovery
arXiv:2608. 08704v1 Announce Type: cross Abstract: Kernel spectral clustering with a single bandwidth can be inadequate for data exhibiting multiple characteristic pairwise-distance scales, a problem particularly prevalent in the high-dimensional regime.
arXiv Computer Vision
Sep 15
Accelerating HKTex without Mesh Eigensystems: Local Unfolding and Randomized Thermal Features
arXiv:2609.14105v1 Announce Type: new Abstract: Heat Kernel Textures (HKTex) represent surface appearance with intrinsic anisotropic kernels, but evaluate them using 50 global Laplace-Beltrami eige...
arXiv Machine Learning
Jul 27
gp2Scale: A Class of Compactly Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points
arXiv:2512. 06143v2 Announce Type: replace Abstract: Despite a large corpus of recent work on scaling up Gaussian processes, a stubborn trade-off between computational speed, prediction and uncertainty quantification accuracy, and customizability persists.
arXiv Machine Learning
Sep 1
Joint Spatiotemporal Spectral Neural Operators for Learning PDEs on Irregular Domains
arXiv:2608.29892v1 Announce Type: new Abstract: Learning solution operators for partial differential equations (PDEs) on irregular and geometry-dependent domains remains a central challenge in scient...