17 citations · 57 across the 16 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Dataset Condensation with Latent Quantile Matching
Wei Wei, Tom De Schepper, Kevin Mets
Dataset condensation (DC) methods aim to learn a smaller synthesized dataset with informative data records to accelerate the training of machine learning models. Current distributi…
Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition
Wei Wei, Tom De Schepper, Kevin Mets
Continual learning (CL) is the research field that aims to build machine learning models that can accumulate knowledge continuously over different tasks without retraining from scr…