Results 281 to 290 of about 77,553 (343)

LAFS: A Fast, Differentiable Approach to Feature Selection Using Learnable Attention. [PDF]

Entropy (Basel)
Topçuoğlu H, Evren A, Tuna E, Ustaoğlu E.   +3 more
europepmc   +1 more source

Inertial Sensor-Based Recognition of Field Hockey Activities Using a Hybrid Feature Selection Framework. [PDF]

Sensors (Basel)
Shahar N, As'ari MA, Mohd Ghazali MH, Zainal N, Zulkifley MA, Ibrahim AA, Omar Z, Rahmat MS, Gan KB, Mohamed Moubark A.   +9 more
europepmc   +1 more source

Taming the Curse of Dimensionality in the Generation Expansion Planning Problem

, 2018
Abeer Almaimouni
openalex   +1 more source

Learning dynamical systems with hit-and-run random feature maps. [PDF]

Nat Commun
Mandal P, Gottwald GA.
europepmc   +1 more source

Breaking the curse of dimensionality based on tensor train : models and algorithms

, 2019
Yassine Zniyed
openalex   +1 more source

Overcoming the Curse of Dimensionality with Synolitic AI

Alexey Zaikin, Ivan Sviridov, Artem Sosedka, Anastasia Linich, Ruslan Nasyrov, Evgeny M. Mirkes, Tatiana Tyukina   +6 more
openalex   +2 more sources

Aspects of approximate optimisation: overcoming the curse of dimensionality and design of experiments

, 2007
Sophie Trichon, M.H.A. Bonte, Jean‐Philippe Ponthot, van den Ton Boogaard   +3 more
openalex   +1 more source

Essays on horizontal merger simulation: the curse of dimensionality, retail price discrimination, and supply channel stage-games

, 2007
Geoffrey M. Pofahl
openalex   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality

IEEE/CAA Journal of Automatica Sinica, 2023
The curse of dimensionality refers to the problem of increased sparsity and computational complexity when dealing with high-dimensional data. In recent years, the types and variables of industrial data have increased significantly, making data-driven ...
Xiaoyu Jiang, Xiangyi Kong, Zhiqiang Ge
semanticscholar   +1 more source

How to Overcome Curse-of-Dimensionality for Out-of-Distribution Detection?

AAAI Conference on Artificial Intelligence, 2023
Machine learning models deployed in the wild can be challenged by out-of-distribution (OOD) data from unknown classes. Recent advances in OOD detection rely on distance measures to distinguish samples that are relatively far away from the in-distribution
Soumya Suvra Ghosal, Yiyou Sun, Yixuan Li   +2 more
semanticscholar   +1 more source