Johan A. K. Suykens

ORCID

Publication Activity (10 Years)

Years Active: 1994-2024
Publications (10 Years): 194

Top Venues

CoRR

IEEE Trans. Neural Networks Learn. Syst.

IJCNN

Neurocomputing

Publications

Zhongjie Shi, Fanghui Liu, Yuan Cao, Johan A. K. Suykens
Can overfitted deep neural networks in adversarial training generalize? - An approximation viewpoint. CoRR (2024)
Bram De Cooman, Johan A. K. Suykens
A Dual Perspective of Reinforcement Learning for Imposing Policy Constraints. CoRR (2024)
Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes. CoRR (2024)
Qinghua Tao, Francesco Tonin, Alex Lambert, Yingyi Chen, Panagiotis Patrinos, Johan A. K. Suykens
Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method. CoRR (2024)
Joran Michiels, Johan A. K. Suykens, Maarten De Vos
Explaining the model and feature dependencies by decomposition of the Shapley value. Decis. Support Syst. 182 (2024)
Francesco Tonin, Qinghua Tao, Panagiotis Patrinos, Johan A. K. Suykens
Deep Kernel Principal Component Analysis for multi-level feature learning. Neural Networks 170 (2024)
Zhongjie Shi, Jun Fan, Linhao Song, Ding-Xuan Zhou, Johan A. K. Suykens
Nonlinear functional regression by functional deep neural network with kernel embedding. CoRR (2024)
Qinghua Tao, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
Tensor-based multi-view spectral clustering via shared latent space. Inf. Fusion 108 (2024)
Qinghua Tao, Xiangming Xi, Jun Xu, Johan A. K. Suykens
Sparsity via Sparse Group k-max Regularization. CoRR (2024)
Yingyi Chen, Shell Xu Hu, Xi Shen, Chunrong Ai, Johan A. K. Suykens
Compressing Features for Learning With Noisy Labels. IEEE Trans. Neural Networks Learn. Syst. 35 (2) (2024)
Joris Depoortere, Johan Driesen, Johan A. K. Suykens, Hussain Syed Kazmi
SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe. CoRR (2024)
Sonny Achten, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification. AAAI (2024)
Sonny Achten, Francesco Tonin, Volkan Cevher, Johan A. K. Suykens
HeNCler: Node Clustering in Heterophilous Graphs through Learned Asymmetric Similarity. CoRR (2024)
Fan He, Mingzhen He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens
Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning. CoRR (2024)
Joran Michiels, Maarten De Vos, Johan A. K. Suykens
Increasing Performance And Sample Efficiency With Model-agnostic Interactive Feature Attributions. CoRR (2023)
Sonny Achten, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
Semi-Supervised Classification with Graph Convolutional Kernel Machines. CoRR (2023)
Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens
Multi-view kernel PCA for time series forecasting. Neurocomputing 554 (2023)
Konstantinos Kontras, Christos Chatzichristos, Huy Phan, Johan A. K. Suykens, Maarten De Vos
CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities. CoRR (2023)
Mihaly Novak, Rocco Langone, Carlos Alzate, Johan A. K. Suykens
Accelerated sparse Kernel Spectral Clustering for large scale data clustering problems. CoRR (2023)
Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens
Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation. CoRR (2023)
Francesco Tonin, Alex Lambert, Panagiotis Patrinos, Johan A. K. Suykens
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms. ICML (2023)
Sonny Achten, Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens
Duality in Multi-View Restricted Kernel Machines. CoRR (2023)
Yingyi Chen, Xi Shen, Yahui Liu, Qinghua Tao, Johan A. K. Suykens
Jigsaw-ViT: Learning jigsaw puzzles in vision transformer. Pattern Recognit. Lett. 166 (2023)
Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens
Multi-view Kernel PCA for Time series Forecasting. CoRR (2023)
Jiani Liu, Qinghua Tao, Ce Zhu, Yipeng Liu, Johan A. K. Suykens
Tensorized LSSVMs for Multitask Regression. CoRR (2023)
Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens
Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation. NeurIPS (2023)
Qinghua Tao, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
Nonlinear SVD with Asymmetric Kernels: feature learning and asymmetric Nyström method. CoRR (2023)
Henri De Plaen, Johan A. K. Suykens
A Dual Formulation for Probabilistic Principal Component Analysis. CoRR (2023)
Fan He, Mingzhen He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens
Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels. CoRR (2023)
Mingzhen He, Fan He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens
Learning With Asymmetric Kernels: Least Squares and Feature Interpretation. IEEE Trans. Pattern Anal. Mach. Intell. 45 (8) (2023)
Francesco Tonin, Alex Lambert, Panagiotis Patrinos, Johan A. K. Suykens
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms. CoRR (2023)
Henri De Plaen, Pierre-François De Plaen, Johan A. K. Suykens, Marc Proesmans, Tinne Tuytelaars, Luc Van Gool
Unbalanced Optimal Transport: A Unified Framework for Object Detection. CoRR (2023)
Jiani Liu, Qinghua Tao, Ce Zhu, Yipeng Liu, Johan A. K. Suykens
Tensorized LSSVMS For Multitask Regression. ICASSP (2023)
Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers. CoRR (2023)
Joran Michiels, Maarten De Vos, Johan A. K. Suykens
Explaining the Model and Feature Dependencies by Decomposition of the Shapley Value. CoRR (2023)
Francesco Tonin, Qinghua Tao, Panagiotis Patrinos, Johan A. K. Suykens
Deep Kernel Principal Component Analysis for Multi-level Feature Learning. CoRR (2023)
Jiani Liu, Qinghua Tao, Ce Zhu, Yipeng Liu, Xiaolin Huang, Johan A. K. Suykens
Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs. CoRR (2023)
Konstantinos Theodorakos, Oscar Mauricio Agudelo, Joachim Schreurs, Johan A. K. Suykens, Bart De Moor
Island Transpeciation: A Co-Evolutionary Neural Architecture Search, Applied to Country-Scale Air-Quality Forecasting. IEEE Trans. Evol. Comput. 27 (4) (2023)
Henri De Plaen, Pierre-François De Plaen, Johan A. K. Suykens, Marc Proesmans, Tinne Tuytelaars, Luc Van Gool
Unbalanced Optimal Transport: A Unified Framework for Object Detection. CVPR (2023)
Qinghua Tao, Zhen Li, Jun Xu, Shu Lin, Bart De Schutter, Johan A. K. Suykens
Short-Term Traffic Flow Prediction Based on the Efficient Hinging Hyperplanes Neural Network. IEEE Trans. Intell. Transp. Syst. 23 (9) (2022)
Yingyi Chen, Xi Shen, Yahui Liu, Qinghua Tao, Johan A. K. Suykens
Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer. CoRR (2022)
Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond. IEEE Trans. Pattern Anal. Mach. Intell. 44 (10) (2022)
Fanghui Liu, Johan A. K. Suykens, Volkan Cevher
On the Double Descent of Random Features Models Trained with SGD. NeurIPS (2022)
Bram De Cooman, Johan A. K. Suykens, Andreas Ortseifen
Enforcing Hard State-Dependent Action Bounds on Deep Reinforcement Learning Policies. LOD (2) (2022)
Mingzhen He, Fan He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens
Learning with Asymmetric Kernels: Least Squares and Feature Interpretation. CoRR (2022)
Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines. IEEE Trans. Pattern Anal. Mach. Intell. 44 (11) (2022)
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Nyström landmark sampling and regularized Christoffel functions. Mach. Learn. 111 (6) (2022)
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems. SIAM J. Math. Data Sci. 4 (3) (2022)
Yingyi Chen, Shell Xu Hu, Xi Shen, Chunrong Ai, Johan A. K. Suykens
Compressing Features for Learning with Noisy Labels. CoRR (2022)
Arun Pandey, Hannes De Meulemeester, Henri De Plaen, Bart De Moor, Johan A. K. Suykens
Recurrent Restricted Kernel Machines for Time-series Forecasting. ESANN (2022)
Arun Pandey, Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Disentangled Representation Learning and Generation With Manifold Optimization. Neural Comput. 34 (10) (2022)
Qinghua Tao, Li Li, Xiaolin Huang, Xiangming Xi, Shuning Wang, Johan A. K. Suykens
Piecewise Linear Neural Networks and Deep Learning. CoRR (2022)
Michaël Fanuel, Antoine Aspeel, Jean-Charles Delvenne, Johan A. K. Suykens
Positive Semi-definite Embedding for Dimensionality Reduction and Out-of-Sample Extensions. SIAM J. Math. Data Sci. 4 (1) (2022)
Qinghua Tao, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
Tensor-based Multi-view Spectral Clustering via Shared Latent Space. CoRR (2022)
Qinghua Tao, Jun Xu, Zhen Li, Na Xie, Shuning Wang, Xiaoli Li, Johan A. K. Suykens
Toward Deep Adaptive Hinging Hyperplanes. IEEE Trans. Neural Networks Learn. Syst. 33 (11) (2022)
Johan A. K. Suykens
Kernel Machines in Time (Invited Talk). TIME (2021)
Marcin Orchel, Johan A. K. Suykens
Improved Update Rule and Sampling of Stochastic Gradient Descent with Extreme Early Stopping for Support Vector Machines. LOD (2021)
Xin Ma, Mei Xie, Johan A. K. Suykens
A novel neural grey system model with Bayesian regularization and its applications. Neurocomputing 456 (2021)
Joachim Schreurs, Iwein Vranckx, Mia Hubert, Johan A. K. Suykens, Peter J. Rousseeuw
Outlier detection in non-elliptical data by kernel MRCD. Stat. Comput. 31 (5) (2021)
Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens
The Bures Metric for Generative Adversarial Networks. ECML/PKDD (2) (2021)
Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints. Neural Networks 142 (2021)
Francesco Tonin, Arun Pandey, Panagiotis Patrinos, Johan A. K. Suykens
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine. CoRR (2021)
Brecht Evens, Puya Latafat, Andreas Themelis, Johan A. K. Suykens, Panagiotis Patrinos
Neural Network Training as an Optimal Control Problem : - An Augmented Lagrangian Approach -. CDC (2021)
Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens
Generalization Properties of hyper-RKHS and its Applications. J. Mach. Learn. Res. 22 (2021)
Francesco Tonin, Arun Pandey, Panagiotis Patrinos, Johan A. K. Suykens
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine. IJCNN (2021)
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Diversity Sampling is an Implicit Regularization for Kernel Methods. SIAM J. Math. Data Sci. 3 (1) (2021)
Maximilian Lucassen, Johan A. K. Suykens, Kim Batselier
Tensor Network Kalman Filtering for Large-Scale LS-SVMs. CoRR (2021)
Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens
Towards Deterministic Diverse Subset Sampling. CoRR (2021)
Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens
Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures. AISTATS (2021)
Joachim Schreurs, Hannes De Meulemeester, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks. CoRR (2021)
Joachim Schreurs, Hannes De Meulemeester, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks. LOD (2021)
Kevin Villalobos, Johan A. K. Suykens, Arantza Illarramendi
A flexible alarm prediction system for smart manufacturing scenarios following a forecaster-analyzer approach. J. Intell. Manuf. 32 (5) (2021)
Lynn Houthuys, Johan A. K. Suykens
Tensor-based restricted kernel machines for multi-view classification. Inf. Fusion 68 (2021)
Fanghui Liu, Johan A. K. Suykens, Volkan Cevher
On the Double Descent of Random Features Models Trained with SGD. CoRR (2021)
Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens
Analysis of regularized least-squares in reproducing kernel Kreĭn spaces. Mach. Learn. 110 (6) (2021)
David Winant, Joachim Schreurs, Johan A. K. Suykens
Latent Space Exploration Using Generative Kernel PCA. CoRR (2021)
Arun Pandey, Joachim Schreurs, Johan A. K. Suykens
Generative Restricted Kernel Machines: A framework for multi-view generation and disentangled feature learning. Neural Networks 135 (2021)
Yingyi Chen, Xi Shen, Shell Xu Hu, Johan A. K. Suykens
Boosting Co-teaching with Compression Regularization for Label Noise. CoRR (2021)
Qinghua Tao, Zhen Li, Jun Xu, Na Xie, Shuning Wang, Johan A. K. Suykens
Learning with continuous piecewise linear decision trees. Expert Syst. Appl. 168 (2021)
Yingyi Chen, Xi Shen, Shell Xu Hu, Johan A. K. Suykens
Boosting Co-Teaching With Compression Regularization for Label Noise. CVPR Workshops (2021)
Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens
Kernel regression in high dimensions: Refined analysis beyond double descent. AISTATS (2021)
Zahra Karevan, Johan A. K. Suykens
Transductive LSTM for time-series prediction: An application to weather forecasting. Neural Networks 125 (2020)
Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens
Kernel regression in high dimension: Refined analysis beyond double descent. CoRR (2020)
Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens
Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces. CoRR (2020)
Marcin Orchel, Johan A. K. Suykens
Fast Hyperparameter Tuning for Support Vector Machines with Stochastic Gradient Descent. LOD (2) (2020)
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems. CoRR (2020)
Siamak Mehrkanoon, Xiaolin Huang, Johan A. K. Suykens
Learning from partially labeled data. ESANN (2020)
Fanghui Liu, Xiaolin Huang, Lei Shi, Jie Yang, Johan A. K. Suykens
A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels. IEEE Trans. Neural Networks Learn. Syst. 31 (8) (2020)
Henri De Plaen, Michaël Fanuel, Johan A. K. Suykens
Wasserstein Exponential Kernels. CoRR (2020)
Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens
Ensemble Kernel Methods, Implicit Regularization and Determinental Point Processes. CoRR (2020)
Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe
A Theoretical Framework for Target Propagation. NeurIPS (2020)
Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens
The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks. CoRR (2020)
Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens
Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond. CoRR (2020)
Henri De Plaen, Michaël Fanuel, Johan A. K. Suykens
Wasserstein Exponential Kernels. IJCNN (2020)
Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe
A Theoretical Framework for Target Propagation. CoRR (2020)
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Diversity sampling is an implicit regularization for kernel methods. CoRR (2020)
Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines. CoRR (2020)
Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan A. K. Suykens
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling. AAAI (2020)
Joachim Schreurs, Iwein Vranckx, Bart De Ketelaere, Mia Hubert, Johan A. K. Suykens, Peter J. Rousseeuw
Outlier detection in non-elliptical data by kernel MRCD. CoRR (2020)
Arun Pandey, Joachim Schreurs, Johan A. K. Suykens
Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality. CoRR (2020)

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Publication Activity (10 Years)

Top Topics

Top Venues

Johan A. K. Suykens