activity
20212024
most citedInferring the relationship between soil temperature and the normalized difference vegetation index with machine learning

22 citations · 41 across the 6 of their papers we have counts for

collaborators

6 papers

cs.NI2024

Designing, Developing, and Validating Network Intelligence for Scaling in Service-Based Architectures based on Deep Reinforcement Learning

Paola Soto, Miguel Camelo, Danny De Vleeschauwer +7

Automating network processes without human intervention is crucial for the complex Sixth Generation (6G) environment. Thus, 6G networks must advance beyond basic automation, relyin…

cs.LG2023★ 22 cited

Inferring the relationship between soil temperature and the normalized difference vegetation index with machine learning

Steven Mortier, Amir Hamedpour, Bart Bussmann +5

Changes in climate can greatly affect the phenology of plants, which can have important feedback effects, such as altering the carbon cycle. These phenological feedback effects are…

cs.CV2023★ 15 cited

An Encoding Framework for Binarized Images using HyperDimensional Computing

Laura Smets, Werner Van Leekwijck, Ing Jyh Tsang +1

Hyperdimensional Computing (HDC) is a brain-inspired and light-weight machine learning method. It has received significant attention in the literature as a candidate to be applied…

cs.AI2022★ 1 cited

Structured Exploration Through Instruction Enhancement for Object Navigation

Matthias Hutsebaut-Buysse, Kevin Mets, Tom De Schepper +1

Finding an object of a specific class in an unseen environment remains an unsolved navigation problem. Hence, we propose a hierarchical learning-based method for object navigation.…

cs.LG2022★ 3 cited

An Analysis of Discretization Methods for Communication Learning with Multi-Agent Reinforcement Learning

Astrid Vanneste, Simon Vanneste, Kevin Mets +4

Communication is crucial in multi-agent reinforcement learning when agents are not able to observe the full state of the environment. The most common approach to allow learned comm…

cs.LG2021

A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark

Amber Cassimon, Reinout Eyckerman, Siegfried Mercelis +2

In this paper, the authors investigate the Deep Sea Treasure (DST) problem as proposed by Vamplew et al. Through a number of proofs, the authors show the original DST problem to be…