Results 111 to 120 of about 97,243 (265)

Self-supervised learning

, 2020
This talk was presented for the Machine Learning Reading Group of the Wellcome Trust EPSRC for Interventional and Surgical Sciences of University College London, 11 February 2020.I gave an overview of self-supervised learning and some applications to computer vision and medical images.
openaire   +1 more source

Self-Supervised Learning for User Localization

2024 International Conference on Computing, Networking and Communications (ICNC)
Machine learning techniques have shown remarkable accuracy in localization tasks, but their dependency on vast amounts of labeled data, particularly Channel State Information (CSI) and corresponding coordinates, remains a bottleneck. Self-supervised learning techniques alleviate the need for labeled data, a potential that remains largely untapped and ...
Ankan Dash, Jingyi Gu, Guiling Wang 0001, Nirwan Ansari   +3 more
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Self supervised learning based emotion recognition using physiological signals

Frontiers in Human Neuroscience
IntroductionThe significant role of emotional recognition in the field of human-machine interaction has garnered the attention of many researchers. Emotion recognition based on physiological signals can objectively reflect the most authentic emotional ...
Min Zhang, YanLi Cui
doaj   +1 more source

High‐Performance Transparent, Deformable, and Recoverable Biomimetic Stevia–PVA Hydrogel Triboelectric Nanogenerator with Machine Learning‐Assisted Motion Recognition

Advanced Materials, EarlyView.
A transparent, deformable stevia–PVA hydrogel triboelectric nanogenerator delivers significantly enhanced mechanical strength and electrical output through biomimetic hydrogen‐bonded networks. Coupled with machine learning–assisted signal recognition, the self‐powered hydrogel enables accurate human‐motion sensing for intelligent wearable and IoT ...
Thien Trung Luu, Bui Minh Quang, Toan Minh Pham, Jinsoo Kim, Kyungwho Choi, Dukhyun Choi   +5 more
wiley   +1 more source

Nanomaterial Integration at Liquid–Liquid Interfaces for Green Catalysis

Advanced Materials, EarlyView.
Functional nanomaterials assembled at liquid–liquid interfaces create dual‐role platforms serving as emulsion stabilizers and catalytic sites, offering enhanced reaction kinetics with improved catalyst recovery and recyclability. This review examines design strategies, structure‐performance relationships, and industrial implementation prospects of ...
Bokgi Seo, Jaewon Shin, Minkyoung Jang, Kyoungho Choi, Tengfei Pang, Fangrui Zhong, Jin Woong Kim   +6 more
wiley   +1 more source

Thiolated Polymers in 3D Bioprinting: Control of Gelation

Advanced Materials, EarlyView.
Thiolated polymers are established as programmable bioinks for 3D bioprinting, integrating versatile crosslinking chemistries with redox‐responsive control. This work demonstrates how molecular design and external triggers define gelation kinetics, printability windows, and structural fidelity, enabling stable, high‐resolution constructs and advancing ...
Soheil Haddadzadegan, Flavia Laffleur, Andreas Bernkop‐Schnürch   +2 more
wiley   +1 more source

Self‐supervised multi‐view clustering in computer vision: A survey

IET Computer Vision
In recent years, multi‐view clustering (MVC) has had significant implications in the fields of cross‐modal representation learning and data‐driven decision‐making.
Jiatai Wang, Zhiwei Xu, Xuewen Yang, Hailong Li, Bo Li, Xuying Meng   +5 more
doaj   +1 more source

Self-Supervised Attentive Feature Learning for Alzheimer’s Disease Detection

International Journal of Interactive Multimedia and Artificial Intelligence
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that leads to memory loss and a decline in cognitive abilities. It primarily affects older adults and is the most common cause of dementia.
Hela Elmannai, Nasir Saleem, Sami Bourouis, Reem Ibrahim Alkanhel   +3 more
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Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI‐Enabled Smart Textiles

Advanced Materials, EarlyView.
Thermally drawn multimaterial fibers are rapidly advancing as intelligent structural units for next‐generation smart textiles. Integrating multimaterial architectures with neuromorphic and spiking‐neural‐network principles enables fabrics that can sense, compute, and adapt autonomously.
Vuong Dinh Trung, Jiaxiang Yi, Hieu Nguyen‐Duc, Tien Nu Hoang Lo, Weili Zhao, Chaoqun Dong, Fabien Sorin, Baris Caglar, Lei Wei, Tung Nguyen‐Dang   +9 more
wiley   +1 more source

Seismic Blind Deconvolution Based on Self-Supervised Machine Learning

Applied Sciences
Seismic deconvolution is a useful tool in seismic data processing. Classical non-machine learning deconvolution methods usually apply quite a few constraints to both wavelet inversion and reflectivity inversion.
Xia Yin, Wenhao Xu, Zhifang Yang, Bangyu Wu   +3 more
doaj   +1 more source