Methods › Computer Vision › Convolutional Neural Networks › MCKERNEL

MCKERNEL

2 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

McKernel introduces a framework to use kernel approximates in the mini-batch setting with Stochastic Gradient Descent (SGD) as an alternative to Deep Learning.

The core library was developed in 2014 as integral part of a thesis of Master of Science [1,2] at Carnegie Mellon and City University of Hong Kong. The original intend was to implement a speedup of Random Kitchen Sinks (Rahimi and Recht 2007) by writing a very efficient HADAMARD tranform, which was the main bottleneck of the construction. The code though was later expanded at ETH Zürich (in McKernel by Curtó et al. 2017) to propose a framework that could explain both Kernel Methods and Neural Networks. This manuscript and the corresponding theses, constitute one of the first usages (if not the first) in the literature of FOURIER features and Deep Learning; which later got a lot of research traction and interest in the community.

More information can be found in this presentation that the first author gave at ICLR 2020 iclr2020_DeCurto.

[1] https://www.curto.hk/c/decurto.pdf

[2] https://www.zarza.hk/z/dezarza.pdf

Source: McKernel: A Library for Approximate Kernel Expansions in...See Code · curto2/mckernel

Papers archive 2025-07-28

2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
CPU1
General Classification1

Usage over time archive 2025-07-28

Papers per year tagged with MCKERNEL: 2017 to 2019, peak 1 1 0 2017: 1 paper 2017 2018: 0 papers 2018 2019: 1 paper 2019
Papers per year the archive tags with this method, by the paper's archive date (2 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Convolutional Neural Networks

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