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ORCIDJohan A. K. Suykens
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- affiliation: KU Leuven, Department of Electrical Engineering, Belgium
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2020 – today
- 2024
[j196]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: 114234 (2024)- [j195]Qinghua Tao, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Tensor-based multi-view spectral clustering via shared latent space. Inf. Fusion 108: 102405 (2024) - [j194]Francesco Tonin, Qinghua Tao, Panagiotis Patrinos, Johan A. K. Suykens:
Deep Kernel Principal Component Analysis for multi-level feature learning. Neural Networks 170: 578-595 (2024) - [j193]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): 2124-2138 (2024) - [c191]Sonny Achten, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification. AAAI 2024: 10766-10774 - [c190]Qinghua Tao, Xiangming Xi, Jun Xu, Johan A. K. Suykens:
Sparsity via Sparse Group k-max Regularization. ACC 2024: 1411-1416 - [c189]Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens:
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes. ICML 2024 - [c188]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. ICML 2024 - [i85]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 abs/2401.02890 (2024) - [i84]Zhongjie Shi, Fanghui Liu, Yuan Cao, Johan A. K. Suykens:
Can overfitted deep neural networks in adversarial training generalize? - An approximation viewpoint. CoRR abs/2401.13624 (2024) - [i83]Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens:
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes. CoRR abs/2402.01476 (2024) - [i82]Qinghua Tao, Xiangming Xi, Jun Xu, Johan A. K. Suykens:
Sparsity via Sparse Group k-max Regularization. CoRR abs/2402.08493 (2024) - [i81]Bram De Cooman, Johan A. K. Suykens:
A Dual Perspective of Reinforcement Learning for Imposing Policy Constraints. CoRR abs/2404.16468 (2024) - [i80]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 abs/2405.14472 (2024) - [i79]Sonny Achten, Francesco Tonin, Volkan Cevher, Johan A. K. Suykens:
HeNCler: Node Clustering in Heterophilous Graphs through Learned Asymmetric Similarity. CoRR abs/2405.17050 (2024) - [i78]Fan He, Mingzhen He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens:
Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning. CoRR abs/2406.01435 (2024) - [i77]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 abs/2406.08748 (2024) - 2023
- [j192]Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens:
Multi-view kernel PCA for time series forecasting. Neurocomputing 554: 126639 (2023) - [j191]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): 10044-10054 (2023) - [j190]Yingyi Chen, Xi Shen, Yahui Liu, Qinghua Tao, Johan A. K. Suykens:
Jigsaw-ViT: Learning jigsaw puzzles in vision transformer. Pattern Recognit. Lett. 166: 53-60 (2023) - [j189]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): 878-892 (2023) - [c187]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: 3198-3207 - [c186]Jiani Liu, Qinghua Tao, Ce Zhu, Yipeng Liu, Johan A. K. Suykens:
Tensorized LSSVMS For Multitask Regression. ICASSP 2023: 1-5 - [c185]Francesco Tonin, Alex Lambert, Panagiotis Patrinos, Johan A. K. Suykens:
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms. ICML 2023: 34379-34393 - [c184]Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens:
Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation. NeurIPS 2023 - [i76]Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens:
Multi-view Kernel PCA for Time series Forecasting. CoRR abs/2301.09811 (2023) - [i75]Sonny Achten, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Semi-Supervised Classification with Graph Convolutional Kernel Machines. CoRR abs/2301.13764 (2023) - [i74]Francesco Tonin, Qinghua Tao, Panagiotis Patrinos, Johan A. K. Suykens:
Deep Kernel Principal Component Analysis for Multi-level Feature Learning. CoRR abs/2302.11220 (2023) - [i73]Jiani Liu, Qinghua Tao, Ce Zhu, Yipeng Liu, Johan A. K. Suykens:
Tensorized LSSVMs for Multitask Regression. CoRR abs/2303.02451 (2023) - [i72]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 abs/2304.06485 (2023) - [i71]Sonny Achten, Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens:
Duality in Multi-View Restricted Kernel Machines. CoRR abs/2305.17251 (2023) - [i70]Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens:
Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation. CoRR abs/2305.19798 (2023) - [i69]Francesco Tonin, Alex Lambert, Panagiotis Patrinos, Johan A. K. Suykens:
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms. CoRR abs/2306.05815 (2023) - [i68]Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers. CoRR abs/2306.07015 (2023) - [i67]Qinghua Tao, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Nonlinear SVD with Asymmetric Kernels: feature learning and asymmetric Nyström method. CoRR abs/2306.07040 (2023) - [i66]Joran Michiels, Maarten De Vos, Johan A. K. Suykens:
Explaining the Model and Feature Dependencies by Decomposition of the Shapley Value. CoRR abs/2306.10880 (2023) - [i65]Joran Michiels, Maarten De Vos, Johan A. K. Suykens:
Increasing Performance And Sample Efficiency With Model-agnostic Interactive Feature Attributions. CoRR abs/2306.16431 (2023) - [i64]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 abs/2307.02402 (2023) - [i63]Henri De Plaen, Johan A. K. Suykens:
A Dual Formulation for Probabilistic Principal Component Analysis. CoRR abs/2307.10078 (2023) - [i62]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 abs/2308.16056 (2023) - [i61]Fan He, Mingzhen He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens:
Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels. CoRR abs/2310.05236 (2023) - [i60]Mihaly Novak, Rocco Langone, Carlos Alzate, Johan A. K. Suykens:
Accelerated sparse Kernel Spectral Clustering for large scale data clustering problems. CoRR abs/2310.13381 (2023) - 2022
- [j188]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Nyström landmark sampling and regularized Christoffel functions. Mach. Learn. 111(6): 2213-2254 (2022) - [j187]Arun Pandey, Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Disentangled Representation Learning and Generation With Manifold Optimization. Neural Comput. 34(10): 2009-2036 (2022) - [j186]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): 7128-7148 (2022) - [j185]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): 7975-7988 (2022) - [j184]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): 153-178 (2022) - [j183]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems. SIAM J. Math. Data Sci. 4(3): 1171-1190 (2022) - [j182]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): 15616-15628 (2022) - [j181]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): 6373-6387 (2022) - [c183]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 - [c182]Bram De Cooman, Johan A. K. Suykens, Andreas Ortseifen:
Enforcing Hard State-Dependent Action Bounds on Deep Reinforcement Learning Policies. LOD (2) 2022: 193-218 - [c181]Fanghui Liu, Johan A. K. Suykens, Volkan Cevher:
On the Double Descent of Random Features Models Trained with SGD. NeurIPS 2022 - [i59]Mingzhen He, Fan He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens:
Learning with Asymmetric Kernels: Least Squares and Feature Interpretation. CoRR abs/2202.01397 (2022) - [i58]Qinghua Tao, Li Li, Xiaolin Huang, Xiangming Xi, Shuning Wang, Johan A. K. Suykens:
Piecewise Linear Neural Networks and Deep Learning. CoRR abs/2206.09149 (2022) - [i57]Yingyi Chen, Shell Xu Hu, Xi Shen, Chunrong Ai, Johan A. K. Suykens:
Compressing Features for Learning with Noisy Labels. CoRR abs/2206.13140 (2022) - [i56]Qinghua Tao, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Tensor-based Multi-view Spectral Clustering via Shared Latent Space. CoRR abs/2207.11559 (2022) - [i55]Yingyi Chen, Xi Shen, Yahui Liu, Qinghua Tao, Johan A. K. Suykens:
Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer. CoRR abs/2207.11971 (2022) - 2021
- [j180]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: 114214 (2021) - [j179]Xin Ma, Mei Xie, Johan A. K. Suykens:
A novel neural grey system model with Bayesian regularization and its applications. Neurocomputing 456: 61-75 (2021) - [j178]Lynn Houthuys, Johan A. K. Suykens:
Tensor-based restricted kernel machines for multi-view classification. Inf. Fusion 68: 54-66 (2021) - [j177]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): 1323-1344 (2021) - [j176]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: 140:1-140:38 (2021) - [j175]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): 1145-1173 (2021) - [j174]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: 177-191 (2021) - [j173]Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints. Neural Networks 142: 661-679 (2021) - [j172]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): 66 (2021) - [j171]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Diversity Sampling is an Implicit Regularization for Kernel Methods. SIAM J. Math. Data Sci. 3(1): 280-297 (2021) - [c180]Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens:
Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures. AISTATS 2021: 388-396 - [c179]Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens:
Kernel regression in high dimensions: Refined analysis beyond double descent. AISTATS 2021: 649-657 - [c178]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: 5136-5143 - [c177]Yingyi Chen, Xi Shen, Shell Xu Hu, Johan A. K. Suykens:
Boosting Co-Teaching With Compression Regularization for Label Noise. CVPR Workshops 2021: 2688-2692 - [c176]Francesco Tonin, Arun Pandey, Panagiotis Patrinos, Johan A. K. Suykens:
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine. IJCNN 2021: 1-8 - [c175]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: 147-161 - [c174]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: 466-480 - [c173]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: 52-66 - [c172]Johan A. K. Suykens:
Kernel Machines in Time (Invited Talk). TIME 2021: 3:1-3:1 - [i54]Francesco Tonin, Arun Pandey, Panagiotis Patrinos, Johan A. K. Suykens:
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine. CoRR abs/2102.08443 (2021) - [i53]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 abs/2104.02373 (2021) - [i52]Yingyi Chen, Xi Shen, Shell Xu Hu, Johan A. K. Suykens:
Boosting Co-teaching with Compression Regularization for Label Noise. CoRR abs/2104.13766 (2021) - [i51]Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Towards Deterministic Diverse Subset Sampling. CoRR abs/2105.13942 (2021) - [i50]David Winant, Joachim Schreurs, Johan A. K. Suykens:
Latent Space Exploration Using Generative Kernel PCA. CoRR abs/2105.13949 (2021) - [i49]Fanghui Liu, Johan A. K. Suykens, Volkan Cevher:
On the Double Descent of Random Features Models Trained with SGD. CoRR abs/2110.06910 (2021) - [i48]Maximilian Lucassen, Johan A. K. Suykens, Kim Batselier:
Tensor Network Kalman Filtering for Large-Scale LS-SVMs. CoRR abs/2110.13501 (2021) - 2020
- [j170]Jun Xu, Qinghua Tao, Zhen Li, Xiangming Xi, Johan A. K. Suykens, Shuning Wang:
Efficient hinging hyperplanes neural network and its application in nonlinear system identification. Autom. 116: 108906 (2020) - [j169]Yunlong Feng, Jun Fan, Johan A. K. Suykens:
A Statistical Learning Approach to Modal Regression. J. Mach. Learn. Res. 21: 2:1-2:35 (2020) - [j168]Zahra Karevan, Johan A. K. Suykens:
Transductive LSTM for time-series prediction: An application to weather forecasting. Neural Networks 125: 1-9 (2020) - [j167]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): 2965-2979 (2020) - [c171]Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan A. K. Suykens:
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling. AAAI 2020: 4844-4851 - [c170]Siamak Mehrkanoon, Xiaolin Huang, Johan A. K. Suykens:
Learning from partially labeled data. ESANN 2020: 493-502 - [c169]Henri De Plaen, Michaël Fanuel, Johan A. K. Suykens:
Wasserstein Exponential Kernels. IJCNN 2020: 1-6 - [c168]Marcin Orchel, Johan A. K. Suykens:
Fast Hyperparameter Tuning for Support Vector Machines with Stochastic Gradient Descent. LOD (2) 2020: 481-493 - [c167]Arun Pandey, Joachim Schreurs, Johan A. K. Suykens:
Robust Generative Restricted Kernel Machines Using Weighted Conjugate Feature Duality. LOD (1) 2020: 613-624 - [c166]Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe:
A Theoretical Framework for Target Propagation. NeurIPS 2020 - [i47]Arun Pandey, Joachim Schreurs, Johan A. K. Suykens:
Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality. CoRR abs/2002.01180 (2020) - [i46]Henri De Plaen, Michaël Fanuel, Johan A. K. Suykens:
Wasserstein Exponential Kernels. CoRR abs/2002.01878 (2020) - [i45]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Diversity sampling is an implicit regularization for kernel methods. CoRR abs/2002.08616 (2020) - [i44]Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens:
Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond. CoRR abs/2004.11154 (2020) - [i43]Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens:
Generalizing Random Fourier Features via Generalized Measures. CoRR abs/2006.00247 (2020) - [i42]Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens:
Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces. CoRR abs/2006.01073 (2020) - [i41]Arun Pandey, Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Disentangled Representation Learning and Generation with Manifold Optimization. CoRR abs/2006.07046 (2020) - [i40]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 abs/2006.09096 (2020) - [i39]Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Ensemble Kernel Methods, Implicit Regularization and Determinental Point Processes. CoRR abs/2006.13701 (2020) - [i38]Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe:
A Theoretical Framework for Target Propagation. CoRR abs/2006.14331 (2020) - [i37]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 abs/2008.02046 (2020) - [i36]Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens:
Kernel regression in high dimension: Refined analysis beyond double descent. CoRR abs/2010.02681 (2020) - [i35]Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens:
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines. CoRR abs/2011.01668 (2020) - [i34]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems. CoRR abs/2011.06964 (2020) - [i33]Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints. CoRR abs/2011.12659 (2020)
2010 – 2019
- 2019
- [j166]Sundaravelpandian Singaravel, Johan A. K. Suykens, Philipp Geyer:
Deep convolutional learning for general early design stage prediction models. Adv. Eng. Informatics 42 (2019) - [j165]Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens:
Robust classification of graph-based data. Data Min. Knowl. Discov. 33(1): 230-251 (2019) - [j164]Ricardo Castro-Garcia, Oscar Mauricio Agudelo, Johan A. K. Suykens:
Impulse response constrained LS-SVM modelling for MIMO Hammerstein system identification. Int. J. Control 92(4): 908-925 (2019) - [j163]Lei Shi, Xiaolin Huang, Yunlong Feng, Johan A. K. Suykens:
Sparse Kernel Regression with Coefficient-based $\ell_q-$regularization. J. Mach. Learn. Res. 20: 161:1-161:44 (2019) - [j162]Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Johan A. K. Suykens:
Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure. IEEE Trans. Neural Networks Learn. Syst. 30(3): 765-776 (2019) - [c165]Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Towards Deterministic Diverse Subset Sampling. BNAIC/BENELEARN 2019 - [c164]David Winant, Joachim Schreurs, Johan A. K. Suykens:
Latent Space Exploration Using Generative Kernel PCA. BNAIC/BENELEARN 2019 - [c163]David Winant, Joachim Schreurs, Johan A. K. Suykens:
Latent Space Exploration Using Generative Kernel PCA. BNAIC/BENELEARN (Selected Papers) 2019: 70-82 - [c162]Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Towards Deterministic Diverse Subset Sampling. BNAIC/BENELEARN (Selected Papers) 2019: 137-151 - [c161]Marcin Orchel, Johan A. K. Suykens:
Axiomatic Kernels on Graphs for Support Vector Machines. ICANN (Workshop) 2019: 685-700 - [i32]Hanyuan Hang, Yingyi Chen, Johan A. K. Suykens:
Two-stage Best-scored Random Forest for Large-scale Regression. CoRR abs/1905.03438 (2019) - [i31]