One of the advancements is from data scientist Paril Ghori, who has effectively used an Autoencoder deep learning model to identify anomalies in residential furnaces. Utilizing cutting-edge machine ...

Abstract: Sparse arrays offer several advantages over other element ... To overcome these limitations, we propose RSB-Net, a region-specific beamformer based on deep reinforcement learning. RSB-Net ...

Abstract: Autoencoders are a type of deep neural network and are widely used for unsupervised ... The resulting pruned model is referred to as a Shapley Value-based Sparse AutoEncoder (SV-SAE). Using ...

School of Science and Technology, Applied Autonomous Sensor Systems, Örebro University, SE-701 82, Örebro, Sweden ...

A new communication-collective system, OptiReduce, speeds up AI and machine learning training across multiple cloud servers by setting time boundaries rather than waiting for every server to catch up, ...

Yu, now an associate professor at the University of California, San Diego (UCSD), is a leader in a field known as “physics-guided deep learning,” having spent years incorporating our knowledge of ...

Mixture-of-Experts (MoE) models are revolutionizing the way we scale AI. By activating only a subset of a model’s components ...

In a recent advance, a multi-disciplinary team of researchers developed a machine learning framework that adapts to changes ...