Nettsider med emneord ?Reinforcement learning?

The goal of the project is to co-develop technology and proposals for regulatory measures to reduce vulnerabilities regarding robotics.

PIRC targets a psychology-inspired computing breakthrough through research combining insight from cognitive psychology with computational intelligence to build models that forecast future events and respond dynamically.

Greenhouse gas seepage into the oceans is a major environmental challenge.

While Machine Learning algorithms have in recent years seen great progress, there are still scenarios in which they fail to be as robust and flexible as animals and humans.

The main objective of this work is to improve the utility of new small satellites for Earth Observation (EO), by researching machine learning techniques to obtain improved and useful detection, classification, and identification capabilities from space.

Accurate mapping of surface greenhouse gas fluxes is necessary for the validation and calibration of climate models. In this project, we develop a novel framework using drone observations and machine learning to estimate greenhouse gas fluxes at a regional scale.

We develop and apply methods based on machine learning for chemistry and materials science. At the method level, our focus is on data (datasets computed with quantum mechanics methods), representations (graphs based on electronic structure theory), and models (graph neural networks and boosted trees).

Humans and animals rely on mental simulations of real-world objects to help them predict the consequences of their actions and generate accurate motor commands in a wide range of situations. Such mental simulations are commonly referred to as internal models.