19 citations · 19 across the 4 of their papers we have counts for
6 papers
Kernel Singular Value Decomposition with Extension to Multiple Data Sources
Xinjie Zeng, Qinghua Tao, Johan Suykens
Kernel Singular Value Decomposition (KSVD) learns a pair of singular vectors w.r.t. an asymmetric kernel matrix, which can be induced by two data sources, e.g., the queries and key…
Deep Adaptive Bayesian Screening
Jade Lejeune Herman, Arno Strouwen, Johan A. K. Suykens +1
We introduce Deep Adaptive Bayesian Screening (DABS), a method for performing adaptive factorial screening in high-dimensional discrete design spaces. DABS learns a policy network…
Learning to Drive Safely with Hybrid Options
Bram De Cooman, Johan Suykens
Out of the many deep reinforcement learning approaches for autonomous driving, only few make use of the options (or skills) framework. That is surprising, as this framework is natu…
Rethinking PCA Through Duality
Jan Quan, Johan Suykens, Panagiotis Patrinos
Motivated by the recently shown connection between self-attention and (kernel) principal component analysis (PCA), we revisit the fundamentals of PCA. Using the difference-of-conve…
SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe
Joris Depoortere, Johan Driesen, Johan Suykens +1
Deep learning models have gained increasing prominence in recent years in the field of solar pho-tovoltaic (PV) forecasting. One drawback of these models is that they require a lot…
A Dual Perspective of Reinforcement Learning for Imposing Policy Constraints
Bram De Cooman, Johan Suykens
Model-free reinforcement learning methods lack an inherent mechanism to impose behavioural constraints on the trained policies. Although certain extensions exist, they remain limit…