← Back to all news
arXiv Machine Learning September 30, 2026 By Maxim Borisyak, Nikita Gladin, Andrey Ustyuzhanin

Meta-learning accelerates detector design optimization

Read the original on arXiv Machine Learning →

The Flow has not summarised this story yet — read it at arXiv Machine Learning.

One email a morning, machine-written

One email a day, machine-written, one click to leave. We never share your address.

Related stories

arXiv Machine Learning
Jun 2

Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA

arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.

By Xiao Jin, Yongxiong Wang, Haobo Liu, Yudong Du, Yukun Du
reinforcement-learningbenchmarks
More like this →
Hugging Face Trending Papers
Jul 7

Efficient Long-Horizon Learning for Learned Optimization

Learned optimization aims to improve upon hand-designed optimizers (e. g.

llmscomputer-vision
More like this →
arXiv Machine Learning
Jul 9

Efficient Long-Horizon Learning for Learned Optimization

arXiv:2607. 06772v1 Announce Type: new Abstract: Learned optimization aims to improve upon hand-designed optimizers (e.

By Xiaolong Huang, Benjamin Th\'erien, James Harrison, Eugene Belilovsky
llmscomputer-vision
More like this →
arXiv Machine Learning
Jun 2

Provable Data Scaling Law for Meta Learning via Complexity Minimization

arXiv:2606. 02008v1 Announce Type: cross Abstract: Pre-training has become a fundamental paradigm in modern machine learning, with one of its key empirical benefits being reduced downstream sample complexity as the scale of pre-training data increases.

By Kazuto Fukuchi, Ryuichiro Hataya, Kota Matsui
More like this →
arXiv Statistics ML
Aug 25

Deep adaptive design with an evidential bias criterion

arXiv:2608.16466v2 Announce Type: replace-cross Abstract: Bayesian optimal experimental design (BOED) aims to collect informative data by optimizing an expected utility reflecting the goals of an exp...

By David Chen, Michael Evans, Xinwei Li, Prateek Bansal, David J. Nott
safety
More like this →
arXiv Machine Learning
Aug 13

A New First-Order Meta-Learning Algorithm with Convergence Guarantees

arXiv:2409. 03682v2 Announce Type: replace Abstract: Learning new tasks by leveraging prior experience is a fundamental trait of intelligent systems.

By El Mahdi Chayti, Martin Jaggi
llmsefficiencysafety
More like this →
About Pricing API Newsletter Sources Privacy Terms Refunds Accessibility Provider info Contact RSS

The Flow links to publishers and never republishes their articles. Summaries are machine-generated.

v1.1.0 · 5f852ea