A Visual Model Of Self-Attention: Transformers Work Differently Now

Early-2026 explainer reframes transformer attention: tokenized text becomes Q/K/V self-attention maps, not linear prediction.
IEEE

Transformer-Based Deep Learning for Mesoscale Eddy Detection in Sea Surface Temperature Maps

Abstract: Mesoscale eddies are dynamic oceanic phenomena significantly influencing marine ecosystems’ energy transfer, nutrients, and biogeochemical cycles. These eddies’ precise identification and ...
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Self-attention in transformers simplified for deep learning

We dive deep into the concept of Self Attention in Transformers! Self attention is a key mechanism that allows models like BERT and GPT to capture long-range dependencies within text, making them ...
Frontiers

Human intention recognition by deep LSTM and transformer networks for real-time human-robot collaboration

Humanoid and Cognitive Robotics Laboratory, Department of Automatics, Biocybernetics, and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia Collaboration between humans and robots is essential for ...
IEEE

An Instructional Optimization Method Based on Bidirectional Transformer and Reinforcement Learning

Abstract: With the rapid development of information technology, personalized education has become a key direction for improving the quality of online learning and optimizing individualized learning ...
Frontiers

Robust automated preclinical fMRI preprocessing via a multi-stage dilated convolutional Swin Transformer affine registration

Introduction: Accurate preprocessing of functional magnetic resonance imaging (fMRI) data is crucial for effective analysis in preclinical studies. Key steps such as denoising, skull-stripping, and ...