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Researcher
Johan Suykens
- Disciplines:Modelling, Applied mathematics in specific fields, Computer architecture and networks, Information sciences, Information systems, Programming languages, Scientific computing, Theoretical computer science, Visual computing, Other information and computing sciences, Control systems, robotics and automation, Design theories and methods, Mechatronics and robotics, Biological system engineering, Computer theory, Signal processing
Affiliations
- Dynamical Systems, Signal Processing and Data Analytics (STADIUS) (Division)
Member
From1 Aug 2020 → Today - ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics (Division)
Member
From1 Jan 2008 → 31 Jul 2020 - Faculty of Engineering Science (Faculty)
Member
From1 Oct 2000 → 30 Sep 2003 - Department of Electrical Engineering (ESAT) (Department)
Member
From1 Oct 1999 → 31 Dec 2007
Projects
11 - 20 of 36
- On Some Theory and Applications of Geometric Deep LearningFrom18 Feb 2019 → 18 Feb 2023Funding: Own budget, for example: patrimony, inscription fees, gifts
- Deep learning models: duality, robustness and generalization propertiesFrom28 Jan 2019 → 28 Jan 2023Funding: Own budget, for example: patrimony, inscription fees, gifts
- Representation Learning with Restricted Kernel MachinesFrom8 Nov 2018 → 5 Jun 2023Funding: Own budget, for example: patrimony, inscription fees, gifts
- Exploring Duality for Future Data-driven ModellingFrom1 Oct 2018 → 30 Sep 2023Funding: H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
- Optimization frameworks for deep kernel machinesFrom1 Oct 2018 → 30 Sep 2022Funding: BOF - Concerted Research Project from 1994
- Semi-Supervised Models with Deep Architectures and ApplicationsFrom1 Oct 2017 → 17 Dec 2020Funding: FWO fellowships
- Trustworthy Kernel Machines: Diversity, Robustness, and DisentanglementFrom1 Oct 2017 → 26 Oct 2021Funding: Own budget, for example: patrimony, inscription fees, gifts
- Deep Restricted Kernel Machines: Methods and FoundationsFrom1 Jan 2017 → 31 Dec 2020Funding: FWO research project (including WEAVE projects)
- Coupled Data-Driven ModelsFrom17 Feb 2016 → 28 Nov 2018Funding: Own budget, for example: patrimony, inscription fees, gifts
- Machine Learning for Energy Performance Prediction in Early Design Stage of BuildingsFrom2 Dec 2015 → 21 Feb 2020Funding: Own budget, for example: patrimony, inscription fees, gifts
Publications
1 - 10 of 410
- Tensor-based multi-view spectral clustering via shared latent space(2024)
Authors: Johan Suykens
- Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification(2024)
Authors: Sonny Achten, Francesco Tonin, Panos Patrinos, Johan Suykens
Pages: 10766 - 10774 - Compressing Features for Learning With Noisy Labels(2024)
Authors: Yingyi Chen, Johan Suykens
Pages: 2124 - 2138 - Deep kernel principal component analysis for multi-level feature learning(2024)
Authors: Panos Patrinos, Johan Suykens
Pages: 578 - 595 - CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities.(2024)
Authors: Christos Chatzichristos, Johan Suykens, Maarten De Vos
Pages: 840 - 849 - Optimization Frameworks for Deep Kernel Machines(2023)
Authors: Francesco Tonin, Johan Suykens, Panos Patrinos
- Island Transpeciation: A Co-Evolutionary Neural Architecture Search, applied to country-scale air-quality forecasting(2023)
Authors: Konstantinos Theodorakos, Mauricio Agudelo Manozca, Joachim Schreurs, Johan Suykens, Bart De Moor
Pages: 878 - 892 - Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms(2023)
Authors: Francesco Tonin, Panos Patrinos, Johan Suykens
Number of pages: 15 - Representation Learning with Restricted Kernel Machines(2023)
Authors: Arun Pandey, Johan Suykens
- Enforcing Hard State-Dependent Action Bounds on Deep Reinforcement Learning Policies(2023)
Authors: Bram De Cooman, Johan Suykens
Pages: 193 - 218Number of pages: 26