Stanford University is offering an online graduate course Artificial Intelligence: Principles and Techniques to provide students with complete knowledge of curr ...

We introduce a methodology for coding Bayesian statistical models in Python with JAX that follows the design pattern of the Stan probabilistic programming language. This allows a direct, line-by-line ...

According to Andrej Karpathy on X, he released a 243-line, dependency-free Python implementation that can both train and run a GPT model, presenting the full algorithmic content without external ...

Abstract: This paper proposes a variational Bayesian inference (VBI) based algorithm for gridless and online estimation of multiple two-dimensional directions of arrival (2D-DOAs), whose number and ...

In many enterprise environments, engineers and technical staff need to find information quickly. They search internal documents such as hardware specifications, project manuals, and technical notes.

In this work, we develop a new framework for designing experiments that are robust to model misspecification through generalised Bayesian inference. This repository contains the files needed to ...

For the past decade, the spotlight in artificial intelligence has been monopolized by training. The breakthroughs have largely come from massive compute clusters, trillion-parameter models, and the ...

This study proposes an important new approach to analyzing cell-count data, which are often undersampled and cannot be accurately assessed using traditional statistical methods. The case studies ...

Cybersecurity researchers have uncovered critical remote code execution vulnerabilities impacting major artificial intelligence (AI) inference engines, including those from Meta, Nvidia, Microsoft, ...