Results 171 to 180 of about 259,134 (247)

Review of Memristors for In‐Memory Computing and Spiking Neural Networks

Advanced Intelligent Systems, EarlyView.
Memristors uniquely enable energy‐efficient, brain‐inspired computing by acting as both memory and synaptic elements. This review highlights their physical mechanisms, integration in crossbar arrays, and role in spiking neural networks. Key challenges, including variability, relaxation, and stochastic switching, are discussed, alongside emerging ...
Mostafa Shooshtari, Teresa Serrano‐Gotarredona, Bernabé Linares‐Barranco   +2 more
wiley   +1 more source

Semi-Supervised Radar Work Mode Recognition Based on Contrastive Learning. [PDF]

Sensors (Basel)
Sun P, Du M, Li Z, Chen X, Shi J.
europepmc   +1 more source

Adaptive Autonomy in Microrobot Motion Control via Deep Reinforcement Learning and Path Planning Synergy

Advanced Intelligent Systems, EarlyView.
This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi, Bairong Zhu, Haoyu Liu, Tingting Yu   +3 more
wiley   +1 more source

Rare event detection by progressive clustering undersampling. [PDF]

PLoS One
Abuzeid A, Jolkver E.
europepmc   +1 more source

Upsampling DINOv2 Features for Unsupervised Vision Tasks and Weakly Supervised Materials Segmentation

Advanced Intelligent Systems, EarlyView.
Feature from recent image foundation models (DINOv2) are useful for vision tasks (segmentation, object localization) with little or no human input. Once upsampled, they can be used for weakly supervised micrograph segmentation, achieving strong results when compared to classical features (blurs, edge detection) across a range of material systems.
Ronan Docherty, Antonis Vamvakeros, Samuel J. Cooper   +2 more
wiley   +1 more source

Similarity-guided swarm of models: enhancing semi-supervised learning in computational pathology. [PDF]

Sci Rep
Weng Z, Pryalukhin A, Hulla W, Bychkov A, Fukuoka J, Schallenberg S, Buchstab O, Klauschen F, Büttner R, Tolkach Y.   +9 more
europepmc   +1 more source

Dual‐Scale Transformer Fusion With Meta Learning for Micro Metastasis Detection in Thyroid Cancer

Advanced Intelligent Systems, EarlyView.
A dual‐scale transformer model enhanced by meta‐learning enables accurate detection of tiny metastatic lesions in thyroid cancer. By combining cellular and tissue‐level features, the method outperforms existing models and shows strong adaptability to rare cases with limited data.
Jingtao Wang, Junhai Shi, Hengli Ni, Limei Zhao, Jing Huang, Jianming Li   +5 more
wiley   +1 more source

Clinically informed semi-supervised learning improves disease annotation and equity from electronic health records: a glaucoma case study. [PDF]

NPJ Digit Med
Moradi M, Shah R, Fujita A, Bineshfar N, Vu DM, Aziz K, Liebman DL, Hashemabad SK, Wang M, Elze T, Eslami M, Zebardast N.   +11 more
europepmc   +1 more source

Phylogenomics, ecomorphological evolution, and historical biogeography in Deuterocohnia (Bromeliaceae: Pitcairnioideae)

American Journal of Botany, EarlyView.
Abstract Premise Species of Deuterocohnia (17 spp.) show extraordinary variation in elevation (0–3900 m a.s.l.) and growth forms, and many have narrow geographic distributions in the west‐central Andes and the Peru‐Chile coast. Previous research using few plastid and nuclear loci failed to produce well‐resolved or supported phylogenies.
Bing Li, Nicole Schütz, Kurt Weising, Georg Zizka, Jacob B. Landis, Thomas J. Givnish   +5 more
wiley   +1 more source

Weight-aware semi-supervised self-ensembling framework for interior decoration style classification. [PDF]

Front Artif Intell
Guo L, Zeng H, Wang J, Liang S, Hang W.
europepmc   +1 more source