The Lancet

Decoding the language of sleep with artificial intelligence

Sleep is one of medicine's underused data streams. Clinically, disturbed sleep has often been treated as a symptom of a disorder, but sleep is also a physiological state in which brain, cardiac, ...

Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding

Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...
Scientific American

‘An AlphaFold 4’—scientists marvel at DeepMind drug spin-off’s exclusive new AI

Isomorphic Lab’s proprietary drug-discovery model is a major advance, but scientists developing open-source tools are left ...

Benchmark of 1.4 million checked protein structures could sharpen AI predictions

University of Missouri researchers have released the world's largest collection of protein models with quality assessment—a ...
2 Minute Medicine

Deep-learning models displayed expert-level accuracy in detecting epileptiform discharges on electroencephalogram (EEG) recordings

Li and colleagues developed a deep-learning model to analyze EEG recordings and detect event-level EEG spikes. 2. The model achieved high accuracy and a low false-positive rate, with only 32% of human ...
IEEE

Sparse Autoencoder-Based Deep Learning Approach for Enhancing End-to-End Communication System Performance

Deep Learning (DL) has emerged as a transformative approach in artificial intelligence, demonstrating remarkable capabilities in solving complex problems once considered unattainable. Its ability to ...
Frontiers

GAME-Net: an ensemble deep learning framework integrating Generative Autoencoders and attention mechanisms for automated brain tumor segmentation in MRI

Introduction: Accurate and early identification of brain tumors is essential for improving therapeutic planning and clinical outcomes. Manual segmentation of Magnetic Resonance Imaging (MRI) remains ...
Scientific Research Publishing

Traffic Speed Prediction Based on Autoencoder and Deep Learning ()

Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction model ...
Scientific Research Publishing

Fanai, H. and Abbasimehr, H. (2023) A Novel Combined Approach Based on Deep Autoencoder and Deep Classifiers for Credit Card Fraud Detection. Expert Systems with Applications ...

ABSTRACT: The rapid growth of technology impacts all aspects of modern life, including banking and financial transactions. While these industries benefit significantly from technological advancements, ...
IEEE

TDAE: Tensored Deep Autoencoder for Classification of Hyperspectral Images

Abstract: Deep learning has achieved outstanding success in the hyperspectral image (HSI) classification task. Almost all the current deep learning methods are used to conduct classification ...