activity
20212026
most citedScalable Reinforcement Learning-based Neural Architecture Search

17 citations · 17 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Intelligent Road Condition Monitoring using 3D In-Air SONAR Sensing

Amber Cassimon, Robin Kerstens, Walter Daems +1

In this paper, we investigate the capabilities of in-air 3D SONAR sensors for the monitoring of road surface conditions. Concretely, we consider two applications: Road material cla…

cs.LG2024

Task Adaptation of Reinforcement Learning-based NAS Agents through Transfer Learning

Amber Cassimon, Siegfried Mercelis, Kevin Mets

Recently, a novel paradigm has been proposed for reinforcement learning-based NAS agents, that revolves around the incremental improvement of a given architecture. We assess the ab…

cs.LG2024

Designing a Classifier for Active Fire Detection from Multispectral Satellite Imagery Using Neural Architecture Search

Amber Cassimon, Phil Reiter, Siegfried Mercelis +1

This paper showcases the use of a reinforcement learning-based Neural Architecture Search (NAS) agent to design a small neural network to perform active fire detection on multispec…

cs.LG2024★ 17 cited

Scalable Reinforcement Learning-based Neural Architecture Search

Amber Cassimon, Siegfried Mercelis, Kevin Mets

In this publication, we assess the ability of a novel Reinforcement Learning-based solution to the problem of Neural Architecture Search, where a Reinforcement Learning (RL) agent…

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…