Talk about kernelization starting in the #PC301workshop now, with a super-apt quote... 🐿

Oldies but goldies: B. Boser, I. Guyon, V. Vapnik, A Training Algorithm for Optimal Margin Classifiers, 1992. Introduced practical large-margin SVM classification together with kernelization. https://t.co/kcR0unUgw0

Great overview of the timeline of polytime kernelization by Bart Jansen at his invited talk at GROW 2022 in Koper

Woodbury formula allows one to compute in two different ways the solution of Ridge regression. It allows one to even use infinite-dimensional feature spaces (p=inf) via kernelization. https://t.co/3c56BFPepI https://t.co/8dXitSEX2C

Great presentation by Fedor Fomin at the FPT Fest 2023 in Bergen in honor of Mike Fellows: Kernelization: past, now and future. Slide: the lost continent of polynomial time preprocessing

Woodbury formula allows one to compute in two different ways the solution of Ridge regression. Allows one to even using infinite dimensional feature spaces (p=inf) via kernelization. https://t.co/3c56BFxD18 https://t.co/8dXitSWxUa

Dynamic Meta-Kernelization.

The IBS discrete mathematics group welcomes Dr. Roohani Sharma, a new research fellow at the IBS Discrete Mathematics Group from May 1, 2025.She is interested in parameterized complexity and kernelization. https://t.co/lZ90JOy5Qd

[CL] DiJiang: Efficient Large Language Models through Compact Kernelization https://t.co/ruYqnwvE2M - This paper presents DiJiang, a novel Frequency Domain Kernelization approach that efficiently transforms a pre-trained vanilla Transformer into a linear complexity

Huawei presents DiJiang: Efficient Large Language Models through Compact Kernelization Achieves comparable performance with LLaMA2-7B on various benchmark while requiring only about 1/50 pretraining cost https://t.co/4AzjmmkYZc

Lokshtanov, D., Panolan, F., Ramanujan, M.S., Saurabh, S.: Lossy kernelization. In: Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2017, Montreal, QC, Canada, 19–23 June 2017, pp. 224–237 (2017) #Artificial_Intelligence

Written by a team of experts in the field, this book introduces the rapidly developing area of preprocessing analysis known as kernelization. https://t.co/dnvLBGaP22

kth mst の前処理と同じ発想だ kernelization っぽい？

https://t.co/c94iiZXDpA, Kernelization: Theory of Parameterized Pr

Manifold Kernelization of Molecular Surface to Encode Quantum Information of Electronic Attributes for Machine Learning #machinelearning #compchem

DiJiang Efficient Large Language Models through Compact Kernelization In an effort to reduce the computational load of Transformers, research on linear attention has gained significant momentum. However, the improvement strategies for attention mechanisms typically

if colonal is pronounces ‘kernel’, why do we say colonization and not ‘kernelization’?

Perfectly Matched Sets in Graphs: Hardness, Kernelization Lower Bound, and FPT and Exact Algorithms. (arXiv:2107.08584v2 [ https://t.co/dR6hrITA5q] UPDATED) https://t.co/FlS7nd5ulB

AMERICA IS BUILT ON KERNELIZATION!

Kernelization dichotomies for hitting minors under structural parameterizations.