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 ...

Researchers in Japan have developed an adaptive motion reproduction system that allows robots to generate human-like movements using surprisingly small amounts of training data. Despite rapid advances ...

This important work introduces a family of interpretable Gaussian process models that allows us to learn and model sequence-function relationships in biomolecules. These models are applied to three ...

Reconstructing high-fidelity and animatable 3D human avatars from visual data is a core task for immersive applications such as virtual reality (VR) and digital content creation. While traditional ...

If you see multiple processes of the same program running on your Windows 11 PC, there is no need to panic, as this is a normal behavior of all Windows computers. However, if you experience ...

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 ...

Abstract: In this paper, we propose a novel Gaussian process-based moving horizon estimation (MHE) framework for unknown nonlinear systems. On the one hand, we approximate the system dynamics by the ...

Developing novel materials drives significant breakthroughs across various engineering fields. Recent advancements in computational resources and techniques have enabled comprehensive material ...

In my prior article for Fast Company Executive Board, I discussed how open-source AI tools are enabling businesses of every size to solve previously unsolvable business challenges without the need for ...

I am a big believer in process mining. My devotion to the approach is partly based on a strong belief in the value of a process orientation; I don’t think organizations can improve their operations ...