Johan Suykens

Full Professor

KU Leuven

Belgium

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Johan Suykens is a Full Professor at KU Leuven in Belgium. His research focuses on nonlinear systems, photovoltaic power forecasting, and advanced machine learning techniques. Recent publications include works on attention-based approaches for Koopman modeling, multimodal frameworks for time series analysis, and deep learning applications in air-quality forecasting.

Johan Suykens

Research Interests

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Articles (15)

An attention-based approach for Koopman modeling and predictive control of nonlinear systems

SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe

Explaining the model and feature dependencies by decomposition of the Shapley value

Deep Kernel Principal Component Analysis for multi-level feature learning

CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities

Jigsaw-ViT: Learning jigsaw puzzles in vision transformer

Island Transpeciation: A Co-Evolutionary Neural Architecture Search, Applied to Country-Scale Air-Quality Forecasting

Learning With Asymmetric Kernels: Least Squares and Feature Interpretation

Towards a Unified Quadrature Framework for Large-Scale Kernel Machines

Toward Deep Adaptive Hinging Hyperplanes

Transfer learning in demand response: A review of algorithms for data-efficient modelling and control

Compressing Features for Learning With Noisy Labels

Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond

Short-Term Traffic Flow Prediction Based on the Efficient Hinging Hyperplanes Neural Network

Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints

Collaborators (3)

Geert Deconinck

Johan Driesen

Bart De Moor

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