Results 91 to 100 of about 546,017 (306)
Contrastive Self-Supervised Learning for Sensor-Based Human Activity Recognition: A Review
IEEE AccessDeep learning models have achieved significant success in human activity recognition, particularly in assisted living and telemonitoring. However, training these models requires substantial amounts of labeled training data, which is time-consuming and ...
Hui Chen +6 more
doaj +1 more source
Q-Match: Self-Supervised Learning by Matching Distributions Induced by a Queue [PDF]
, 2023 Thomas Mulc, Debidatta Dwibedi
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Advanced Functional Materials, EarlyView.Molecular engineering of a nonconjugated radical polymer enables a significant enhancement of the glass transition temperature. The amorphous nature and tunability of the polymer, arising from its nonconjugated backbone, facilitates the fabrication of organic memristive devices with an exceptionally high yield (>95%), as well as substantial ...
Daeun Kim +14 more
wiley +1 more source
Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition [PDF]
, 2022 Xichen Pan +5 more
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Advanced Functional Materials, EarlyView.This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee +17 more
wiley +1 more source
Advanced Functional Materials, EarlyView.Anion‐excessive gel‐based organic synaptic transistors (AEG‐OSTs) that can maintain electrical neutrality are developed to enhance synaptic plasticity and multistate retention. Key improvement is attributed to the maintenance of electrical neutrality in the electrolyte even after electrochemical doping, which reduces the Coulombic force acting on ...
Yousang Won +3 more
wiley +1 more source
ViewMix: Augmentation for Robust Representation in Self-Supervised Learning [PDF]
, 2023 Arjon Das, Xin Zhong
openalex +1 more source
SimPer: Simple Self-Supervised Learning of Periodic Targets [PDF]
, 2022 Yuzhe Yang +6 more
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Artificial Intelligence as the Next Visionary in Liquid Crystal Research
Advanced Functional Materials, EarlyView.The functions of AI in the research laboratory are becoming increasingly sophisticated, allowing the entire process of hypothesis formulation, material design, synthesis, experimental design, and reiterative testing to be automated. In our work, we conceive how the incorporation of AI in the laboratory environment will transform the role and ...
Mert O. Astam +2 more
wiley +1 more source
Adversarial Contrastive Self-Supervised Learning
, 2022 Recently, learning from vast unlabeled data, especially self-supervised learning, has been emerging and attracted widespread attention. Self-supervised learning followed by the supervised fine-tuning on a few labeled examples can significantly improve label efficiency and outperform standard supervised training using fully annotated data. In this work,
Zhu, Wentao +5 more
openaire +2 more sources