no code implementations • 9 Mar 2018 • Hussain Kazmi, Johan Suykens, Johan Driesen
Results show that savings of over 40% are possible with collaborative multi-agent systems making use of either expert knowledge or additional sensors with no loss of occupant comfort.
no code implementations • 15 Oct 2014 • Emanuele Frandi, Ricardo Nanculef, Johan Suykens
Frank-Wolfe algorithms for convex minimization have recently gained considerable attention from the Optimization and Machine Learning communities, as their properties make them a suitable choice in a variety of applications.
no code implementations • 14 Apr 2014 • Andreas Argyriou, Marco Signoretto, Johan Suykens
We study a hybrid conditional gradient - smoothing algorithm (HCGS) for solving composite convex optimization problems which contain several terms over a bounded set.
no code implementations • NeurIPS 2007 • Kristiaan Pelckmans, Johan Suykens, Bart D. Moor
This paper explores the use of a Maximal Average Margin (MAM) optimality principle for the design of learning algorithms.
no code implementations • 28 Sep 2020 • Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan Suykens
However, under certain circumstances, the training of GANs can lead to mode collapse or mode dropping, i. e. the generative models not being able to sample from the entire probability distribution.
no code implementations • 27 Mar 2023 • Konstantinos Kontras, Christos Chatzichristos, Huy Phan, Johan Suykens, Maarten De Vos
The results indicate that training the model on multimodal data does positively influence performance when tested on unimodal data.
Ranked #1 on Sleep Stage Detection on SHHS
no code implementations • 19 Jun 2023 • Joran Michiels, Maarten De Vos, Johan Suykens
In this paper we examine this question and claim that they represent two different explanations which are valid for different end-users: one that explains the model and one that explains the model combined with the feature dependencies in the data.
no code implementations • 28 Jun 2023 • Joran Michiels, Maarten De Vos, Johan Suykens
Additionally we explore how a domain expert can provide feature attributions which are sufficiently correct to improve the model.
no code implementations • 20 Oct 2023 • Mihaly Novak, Rocco Langone, Carlos Alzate, Johan Suykens
This is altered by the modifications reported in this brief that drastically improve the computational characteristics.
no code implementations • 25 Apr 2024 • Bram De Cooman, Johan Suykens
In this work we try to unify these existing techniques and bridge the gap with classical optimization and control theory, using a generic primal-dual framework for value-based and actor-critic reinforcement learning methods.
1 code implementation • 4 Mar 2014 • Marc Claesen, Frank De Smet, Johan Suykens, Bart De Moor
EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models.