Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...

Abstract: In this work, we have developed a variational Bayesian inference theory of elasticity, which is accomplished by using a mixed Variational Bayesian inference Finite Element Method (VBI-FEM) ...

Variational inference is a family of optimisation-based methods for approximating complex posterior distributions in Bayesian models. By transforming inference into an optimisation problem, these ...

PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference, previously implemented in MATLAB. VBMC is an approximate inference method ...

A significant shift is under way in artificial intelligence, and it has huge implications for technology companies big and small. For the past half-decade, most of the focus in AI has been on training ...

Facial expressions in the wild are rarely discrete; they often manifest as compound emotions or subtle variations that challenge the discriminative capabilities of conventional models. While ...

The creators of the open source project vLLM have announced that they transitioned the popular tool into a VC-backed startup, Inferact, raising $150 million in seed funding at an $800 million ...

Animals survive in changing and unpredictable environments by not merely responding to new circumstances, but also, like humans, by forming inferences about their surroundings—for instance, squirrels ...

Forbes contributors publish independent expert analyses and insights. I write about the economics of AI. When OpenAI’s ChatGPT first exploded onto the scene in late 2022, it sparked a global obsession ...