Gaussian Splatting is a cutting-edge 3D representation technique that models a scene as a set of learnable 3D Gaussian primitives. Each Gaussian defines a point in space with position, color, opacity, ...

Pytorch implementation of the paper 'Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video Grounding' (AAAI2024). Implemented an auto ...

Abstract: Inducing-point-based sparse variational approximation scales Gaussian process models to large datasets but tends to overestimate observation noise and underestimate posterior variance.

Abstract: Gaussian process state-space models (GPSSMs) offer a principled framework for learning and inference in nonlinear dynamical systems with uncertainty quantification. However, existing GPSSMs ...

ABSTRACT: This paper introduces a method to develop a common model based on machine learning (ML) that predicts the mechanical behavior of a family with three composite materials. The latter are ...

What if the programming language you rely on most is on the brink of a transformation? For millions of developers worldwide, Python is not just a tool, it’s a cornerstone of their craft, powering ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...