arXiv (All)
open access
Spectral Bounds for Kernel Quadrature
A bottleneck in the theory of kernel methods in machine learning is the storage requirement. To ameliorate this, a standard trick is to replace the kernel with an explicit feature map. Perhaps, the most well known example is the Gaussian kernel which can be expressed in terms of the Fourier features
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- $p$ and $rp$-Spectral Element Methods for Two-dimensional Elliptic Boundary Layer Problems#1077114
- Performance Evaluation of Fast Fourier Transforms on Emerging RISC-V Hardware with Vector Extension Support#618536
- Uniform Non-Localization under Robin Boundary Perturbations: Spectral Splitting and Bounded Eigenspace Complexity#985245
- IS-IS Prefix Attributes for Extended IPv4 and IPv6 Reachability#502051
- FlashOverlap: Minimizing Tail Latency in Communication Overlap for Distributed LLM Training#138898
Record · ID 819992
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive