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.

AlphaGenome: Google’s new AI tool that can decipher and predict DNA changes. How does it work?

A new AI model by Google DeepMind can decipher DNA and predict mutations, opening new doors for disease research. View on euronews ...
Frontiers

Detection of leptomeningeal angiomas in brain MRI of Sturge-Weber syndrome using multi-scale multi-scan Mamba

Objectives: Sturge-Weber syndrome (SWS) is a congenital neurological disorder occurring in the early childhood. Timely diagnosis of SWS is essential for proper medical intervention that prevents the ...
Scientific Research Publishing

Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P. and Shroff, G. (2016) LSTM-Based Encoder-Decoder for Multi-Sensor Anomaly Detection.

ABSTRACT: This work presents an innovative Intrusion Detection System (IDS) for Edge-IoT environments, based on an unsupervised architecture combining LSTM networks and Autoencoders. Deployed on ...
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 ...
Frontiers

Domain adaptive deep possibilistic clustering for EEG-based emotion recognition

In the field of affective computing (Muhl C. and G., 2014), automatic emotion recognition (AER) (Stern, 2002) has gained significant attention (Kim et al., 2013; Zhang et al., 2013), especially for ...
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 ...