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 ...
Opinion
CIOOpinion

AI isn’t failing, people are failing with AI

AI isn’t the problem — rushing it into the wrong tasks without the right data, expertise or guardrails is what makes projects fall apart.
IEEE

Deep Learning-Based Iterative Residual Encoder-Decoder Solution for Electromagnetic Modeling over a Broad Frequency Band

Abstract: In this letter, we propose a deep learning-based iterative residual encoder-decoder method (IRED), which provides an efficient deep learning framework for electromagnetic modeling over a ...
Scientific Research Publishing

Feng, L., Zhao, C. and Sun, Y. (2021) Dual Attention-Based Encoder-Decoder: A Customized Sequence-to-Sequence Learning for Soft Sensor Development. IEEE Transactions on Neural ...

ABSTRACT: In this paper, a novel multilingual OCR (Optical Character Recognition) method for scanned papers is provided. Current open-source solutions, like Tesseract, offer extremely high accuracy ...
ahajournals.org

Abstract P3089: Predicting Reductions In Hemoglobin A1c From Large Dietary Datasets Using Artificial Intelligence Pipeline With Interpretable Encoder-Decoder Deep Neural Networks

Introduction: Artificial intelligence algorithms can help understand and predict the complex interactions between dietary intake and health outcomes, especially from large datasets. Precision ...