17 citations · 57 across the 16 of their papers we have counts for
13 papers · 1 filter
Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review
Bruno Deprez, Wei Wei, Wouter Verbeke +3
Financial institutions are required by regulation to report suspicious financial transactions related to money laundering. Therefore, they need to constantly monitor vast amounts o…
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…
Safety Aware Autonomous Path Planning Using Model Predictive Reinforcement Learning for Inland Waterways
Astrid Vanneste, Simon Vanneste, Olivier Vasseur +7
In recent years, interest in autonomous shipping in urban waterways has increased significantly due to the trend of keeping cars and trucks out of city centers. Classical approache…
An In-Depth Analysis of Discretization Methods for Communication Learning using Backpropagation with Multi-Agent Reinforcement Learning
Astrid Vanneste, Simon Vanneste, Kevin Mets +3
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…
Scalability of Message Encoding Techniques for Continuous Communication Learned with Multi-Agent Reinforcement Learning
Astrid Vanneste, Thomas Somers, Simon Vanneste +4
Many multi-agent systems require inter-agent communication to properly achieve their goal. By learning the communication protocol alongside the action protocol using multi-agent re…
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…
Learning to Communicate with Reinforcement Learning for an Adaptive Traffic Control System
Simon Vanneste, Gauthier de Borrekens, Stig Bosmans +5
Recent work in multi-agent reinforcement learning has investigated inter agent communication which is learned simultaneously with the action policy in order to improve the team rew…
Mixed Cooperative-Competitive Communication Using Multi-Agent Reinforcement Learning
Astrid Vanneste, Wesley Van Wijnsberghe, Simon Vanneste +4
By using communication between multiple agents in multi-agent environments, one can reduce the effects of partial observability by combining one agent's observation with that of ot…
HTMRL: Biologically Plausible Reinforcement Learning with Hierarchical Temporal Memory
Jakob Struye, Kevin Mets, Steven Latré
Building Reinforcement Learning (RL) algorithms which are able to adapt to continuously evolving tasks is an open research challenge. One technology that is known to inherently han…
Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning
Matthias Hutsebaut-Buysse, Kevin Mets, Steven Latré
Reinforcement learning (RL) algorithms typically start tabula rasa, without any prior knowledge of the environment, and without any prior skills. This however often leads to low sa…