1 citations · 1 across the 5 of their papers we have counts for
7 papers
Learning Adaptive SED for heterogeneous load balancing
Sanne van Kempen, Jaron Sanders, Fiona Sloothaak +1
We study a two-server load balancing system with heterogeneous service rates that are a priori unknown to the dispatcher. The goal is to route customers according to the Shortest--…
Dropout Neural Network Training Viewed from a Percolation Perspective
Finley Devlin, Jaron Sanders
In this work, we investigate the existence and effect of percolation in training deep Neural Networks (NNs) with dropout. Dropout methods are regularisation techniques for training…
Asymptotically optimal reinforcement learning in Block Markov Decision Processes
Thomas van Vuren, Fiona Sloothaak, Maarten G. Wolf +1
The curse of dimensionality renders Reinforcement Learning (RL) impractical in many real-world settings with exponentially large state and action spaces. Yet, many environments exh…
In situ fine-tuning of in silico trained Optical Neural Networks
Gianluca Kosmella, Ripalta Stabile, Jaron Sanders
Optical Neural Networks (ONNs) promise significant advantages over traditional electronic neural networks, including ultrafast computation, high bandwidth, and low energy consumpti…
Demonstration of effective UCB-based routing in skill-based queues on real-world data
Sanne van Kempen, Jaron Sanders, Fiona Sloothaak +1
This paper is about optimally controlling skill-based queueing systems such as data centers, cloud computing networks, and service systems. By means of a case study using a real-wo…
Learning payoffs while routing in skill-based queues
Sanne van Kempen, Jaron Sanders, Fiona Sloothaak +1
Motivated by applications in service systems, we consider queueing systems where each customer must be handled by a server with the right skill set. We focus on optimizing the rout…
Estimating the number of clusters of a Block Markov Chain
Thomas van Vuren, Thomas Cronk, Jaron Sanders
Clustering algorithms frequently require the number of clusters to be chosen in advance, but it is usually not clear how to do this. To tackle this challenge when clustering within…