A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...

From autonomous cars to video games, reinforcement learning (machine learning through interaction with environments) can have ...

Abstract: In recent years, Compute-in-memory (CiM) architectures have emerged as a promising solution for deep neural network (NN) accelerators. Multiply-accumulate (MAC) is considered a de facto unit ...

Abstract: Real-time precipitation retrieval is crucial for timely warnings of extreme weather events such as heavy rain or floods. Geostationary satellite observations combined with machine learning ...

Elon Musk said over the long weekend that Tesla aims to restart work on Dojo3, the electric vehicle company’s previously abandoned third-generation AI chip. Only this time, Dojo3 won’t be aimed at ...

SPIDER is a state-of-the-art Python toolkit for probabilistic earthquake location using deep learning and MCMC sampling. It combines neural network-based travel time prediction with scalable Bayesian ...

Low-rank data analysis has emerged as a powerful paradigm across applied mathematics, statistics, and data science. With the rapid growth of modern datasets in size, dimensionality, and complexity, ...

In recent years, the big money has flowed toward LLMs and training; but this year, the emphasis is shifting toward AI inference. LAS VEGAS — Not so long ago — last year, let’s say — tech industry ...