Radio waves enable energy-efficient AI on edge devices without heavy hardware

As drones survey forests, robots navigate warehouses and sensors monitor city streets, more of the world's decision-making is ...

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 ...
IEEE

Using Deep Learning Transformers for Detection of Hedonic Emotional States by Analyzing Eudaimonic Behavior of Online Users

Abstract: Hedonic emotions represent a significant concept in the study of linguistics, encapsulates the patterns of positivity, pleasures, activities, and enjoyment. These emotions play a critical ...
unite

Book Review: Deep Learning Crash Course: A Hands-On, Project-Based Introduction to Artificial Intelligence

Deep Learning Crash Course: A Hands-On, Project-Based Introduction to Artificial Intelligence is written by Giovanni Volpe, Benjamin Midtvedt, Jesús Pineda, Henrik Klein Moberg, Harshith Bachimanchi, ...
Hosted on MSN

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 ...