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.

The 11 runtime attacks breaking AI security — and how CISOs are stopping them

CrowdStrike's 2025 data shows attackers breach AI systems in 51 seconds. Field CISOs reveal how inference security platforms ...
IEEE

ABMF-Net: An Attentive Bayesian Multi-Stage Deep Learning Model for Robust Forecasting of Electricity Price and Demand

Abstract: This article presents a novel deep learning model, the Attentive Bayesian Multi-Stage Forecasting Network (ABMF-Net), designed for robust forecasting of electricity price (USD/MWh) and ...
IEEE

HAT: Hybrid Attention Transformer for Image Restoration

Abstract: Transformer-based methods have shown impressive performance in image restoration tasks, such as image super resolution and denoising. However, we find that these networks can only utilize a ...
EurekAlert!

CornPheno: A game-changer in corn breeding with smartphone-based phenotyping

Corn is one of the world's most important crops, critical for food, feed, and industrial applications. In 2023, corn ...
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 ...
UPI

British media spotlight South Korea's 'Suneung' high difficulty

Dec. 15 (Asia Today) --South Korea's annual College Scholastic Ability Test, known as the Suneung, has drawn international attention after British media outlets highlighted complaints over the ...