Exploring Duality for Future Data-driven Modelling

Duality in Multi-View Restricted Kernel Machines

Autori: Achten, Sonny; Pandey, Arun; De Meulemeester, Hannes; De Moor, Bart; Suykens, Johan A. K.
Pubblicato in: ICML Workshop on Duality for Modern Machine Learning,, Numero 5, 2023
Editore: ICML
DOI: 10.48550/arxiv.2305.17251

A Dual Formulation for Probabilistic Principal Component Analysis

Autori: De Plaen, Henri; Suykens, J
Pubblicato in: Proceedings ol ICML 2023 Workshop on Duality Principles for Modern Machine Learning (DP4ML);, Numero 1, 2023
Editore: DP4ML
DOI: 10.48550/arxiv.2307.10078

Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms

Autori: Tonin, Francesco; Lambert, A; Patrinos, P; Suykens, J
Pubblicato in: International Conference on Machine Learning (ICML 2023), 2023
Editore: PMLR 202

Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine

Autori: Tonin, Francesco; Pandey, Arun; Patrinos, Panagiotis; Suykens, Johan A. K.
Pubblicato in: International Joint Conference on Neural Networks, Numero 4, 2021, ISSN 2161-4393
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/ijcnn52387.2021.9533706

A Theoretical Framework for Target Propagation

Autori: Meulemans, Alexander; Carzaniga, Francesco; Suykens, Johan A.K.; Sacramento, João; Grewe, Benjamin F.
Pubblicato in: Advances in Neural Information Processing Systems, Numero 33, 2020, Pagina/e 20024--20036
Editore: NeurIPS
DOI: 10.5167/uzh-198834

Recurrent Restricted Kernel Machines for Time-series Forecasting

Autori: Pandey, A; De Meulemeester, H; De Plaen, H; De Moor, B; Suykens, Johan
Pubblicato in: European symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Numero ESANN 2022, 2022
Editore: ESANN

Boosting Co-teaching with Compression Regularization for Label Noise

Autori: Yingyi Chen, Xi Shen, Shell Xu Hu, Johan A. K. Suykens
Pubblicato in: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021, Pagina/e 2682-2686, ISBN 978-1-6654-4899-4
Editore: IEEE
DOI: 10.1109/cvprw53098.2021.00302

Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification

Autori: Achten, Sonny; Tonin, F; Patrinos, Panagiotis; Suykens, J
Pubblicato in: AAAI Conference on Artificial Intelligence, Numero 2024, 2024, ISBN 1-57735-887-2
Editore: AAAI Press
DOI: 10.1609/aaai.v38i10.28949

The Bures Metric for Generative Adversarial Networks

Autori: De Meulemeester, Hannes; Schreurs, Joachim; Fanuel, Michaël; De Moor, Bart; Suykens, Johan A. K.
Pubblicato in: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021
Editore: ECML-PKDD 2021

Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes

Autori: Schreurs, Joachim; Fanuel, M; Suykens, J
Pubblicato in: ICML 2020 Workshop on Negative Dependence and Submodularity for ML, Numero 15, 2020
Editore: ICML

Wasserstein Exponential Kernels

Autori: Henri De Plaen, Michael Fanuel, Johan A. K. Suykens
Pubblicato in: 2020 International Joint Conference on Neural Networks (IJCNN), 2020, Pagina/e 1-6, ISBN 978-1-7281-6926-2
Editore: IEEE
DOI: 10.1109/ijcnn48605.2020.9207630

Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks

Autori: Schreurs J., De Meulemeester H., Fanuel M., De Moor B., Suykens J.A.K
Pubblicato in: Conference on Machine Learning, Optimization and Data Science, 2021, ISBN 978-3-030-95469-7
Editore: Springer
DOI: 10.1007/978-3-030-95470-3_35

On the Double Descent of Random Features Models Trained with SGD

Autori: Liu, F; Suykens, Johan; Cevher, V
Pubblicato in: Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 2022, ISBN 9781713871088
Editore: NeurIPS

Tensorized LSSVMs for Multitask Regression

Autori: Liu, J; Tao, Q; Zhu, C; Liu, Y; Suykens, Johan
Pubblicato in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023), 2023, ISBN 978-1-7281-6327-7
Editore: IEEE
DOI: 10.1109/icassp49357.2023.10094580

Fast Adaptive Hinging Hyperplanes

Autori: Qinghua Tao, Jun Xu, Johan A.K. Suykens, Shuning Wang
Pubblicato in: 2018 IEEE Conference on Decision and Control (CDC), 2018, Pagina/e 1482-1487, ISBN 978-1-5386-1395-5
Editore: IEEE
DOI: 10.1109/cdc.2018.8619653

Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures

Autori: Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan Suykens
Pubblicato in: roceedings of The 24th International Conference on Artificial Intelligence and Statistics, Numero PMLR 130, 2021, Pagina/e 388-396
Editore: PMLR

Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers

Autori: Tonin, F; Patrinos, P; Suykens, Johan
Pubblicato in: ECML PKDD 2023 - Workshop on Simplification, Compression, Efficiency and Frugality for Artificial intelligence, 2023
Editore: ECML PKDD

Kernel regression in high dimensions: Refined analysis beyond double descent

Autori: Fanghui Liu, Zhenyu Liao, Johan Suykens
Pubblicato in: Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, Numero PMLR 130, 2021, Pagina/e 649-657
Editore: PMLR

Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation

Autori: Chen, Yingyi; Tao, Qinghua; Tonin, Francesco; Suykens, Johan A. K.
Pubblicato in: Proc. of NeurIPS 2023, Numero 1, 2023
Editore: NeuriPS
DOI: 10.48550/arxiv.2305.19798

Unbalanced Optimal Transport: A Unified Framework for Object Detection

Autori: De Plaen, Henri; De Plaen, P; Suykens, J; Proesmans, M; Tuytelaars, T; Van Gool, L
Pubblicato in: Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern, Numero 5, 2023, ISSN 1063-6919
Editore: IEEE
DOI: 10.1109/cvpr52729.2023.00312

The Bures Metric for Generative Adversarial Networks

Autori: Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens
Pubblicato in: Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part II, Numero 12976, 2021, Pagina/e 52-66, ISBN 978-3-030-86519-1
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-86520-7_4

Latent Space Exploration Using Generative Kernel PCA

Autori: David Winant, Joachim Schreurs, Johan A. K. Suykens
Pubblicato in: Artificial Intelligence and Machine Learning - 31st Benelux AI Conference, BNAIC 2019, and 28th Belgian-Dutch Machine Learning Conference, BENELEARN 2019, Brussels, Belgium, November 6-8, 2019, Revised Selected Papers, Numero 1196, 2020, Pagina/e 70-82, ISBN 978-3-030-65153-4
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-65154-1_5

Robust Generative Restricted Kernel Machines Using Weighted Conjugate Feature Duality

Autori: Arun Pandey, Joachim Schreurs, Johan A. K. Suykens
Pubblicato in: Machine Learning, Optimization, and Data Science - 6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part I, Numero 12565, 2020, Pagina/e 613-624, ISBN 978-3-030-64582-3
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-64583-0_54

Towards Deterministic Diverse Subset Sampling

Autori: J. Schreurs, M. Fanuel, J. A. K. Suykens
Pubblicato in: Artificial Intelligence and Machine Learning - 31st Benelux AI Conference, BNAIC 2019, and 28th Belgian-Dutch Machine Learning Conference, BENELEARN 2019, Brussels, Belgium, November 6-8, 2019, Revised Selected Papers, Numero 1196, 2020, Pagina/e 137-151, ISBN 978-3-030-65153-4
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-65154-1_8

Fast Hyperparameter Tuning for Support Vector Machines with Stochastic Gradient Descent

Autori: Marcin Orchel, Johan A. K. Suykens
Pubblicato in: Machine Learning, Optimization, and Data Science - 6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part II, Numero 12566, 2020, Pagina/e 481-493, ISBN 978-3-030-64579-3
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-64580-9_40

Tensor Learning in Multi-view Kernel PCA

Autori: Lynn Houthuys, Johan A. K. Suykens
Pubblicato in: Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part II, Numero 11140, 2018, Pagina/e 205-215, ISBN 978-3-030-01420-9
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-01421-6_21

Weighted Multi-view Deep Neural Networks for Weather Forecasting

Autori: Zahra Karevan, Lynn Houthuys, Johan A. K. Suykens
Pubblicato in: Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III, Numero 11141, 2018, Pagina/e 489-499, ISBN 978-3-030-01423-0
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-01424-7_48

Axiomatic Kernels on Graphs for Support Vector Machines

Autori: Marcin Orchel, Johan A. K. Suykens
Pubblicato in: Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Numero 11731, 2019, Pagina/e 685-700, ISBN 978-3-030-30492-8
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-30493-5_62

Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs

Autori: Liu, Jiani; Tao, Qinghua; Zhu, Ce; Liu, Yipeng; Huang, Xiaolin; Suykens, Johan A. K.
Pubblicato in: Technical report, Numero 1, 2023
Editore: KU Leuven
DOI: 10.48550/arxiv.2308.16056

Can overfitted deep neural networks in adversarial training generalize? -- An approximation viewpoint

Autori: Shi, Zhongjie; Liu, Fanghui; Cao, Yuan; Suykens, Johan A. K.
Pubblicato in: Internal report, 2024
Editore: KU Leuven
DOI: 10.48550/arxiv.2401.13624

Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure

Autori: Fanghui Liu; Xiaolin Huang; Chen Gong; Jie Yang; Johan A. K. Suykens
Pubblicato in: IEEE Transactions on Neural Networks and Learning Systems, 2019, ISSN 3004-7906
Editore: IEEE
DOI: 10.1109/tnnls.2018.2851305

Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels

Autori: Fan He, Mingzhen He, Lei Shi, Xiaolin Huang, Johan A.K. Suykens
Pubblicato in: Internal Report, 2023
Editore: KU Leuven
DOI: 10.48550/arxiv.2310.05236

Nonlinear SVD with Asymmetric Kernels: feature learning and asymmetric Nyström method

Autori: Tao, Qinghua; Tonin, Francesco; Patrinos, Panagiotis; Suykens, Johan A. K.
Pubblicato in: Internal Report, 2023
Editore: KU Leuven
DOI: 10.48550/arxiv.2306.07040

Functional Output Regression with Infimal Convolution: Exploring the Huber and $ε$-insensitive Losses

Autori: Lambert, Alex; Bouche, Dimitri; Szabo, Zoltan; d'Alché-Buc, Florence
Pubblicato in: https://hal.science/hal-03807108, Numero 1, 2022
Editore: KU Leuven
DOI: 10.48550/arxiv.2206.08220

Nonlinear functional regression by functional deep neural network with kernel embedding

Autori: Shi, Zhongjie; Fan, Jun; Song, Linhao; Zhou, Ding-Xuan; Suykens, Johan A. K.
Pubblicato in: Internal report, Numero 2, 2024
Editore: KU Leuven
DOI: 10.48550/arxiv.2401.02890

A flexible alarm prediction system for smart manufacturing scenarios following a forecaster–analyzer approach

Autori: Kevin Villalobos, Johan Suykens, Arantza Illarramendi
Pubblicato in: Journal of Intelligent Manufacturing, Numero 32/5, 2021, Pagina/e 1323-1344, ISSN 0956-5515
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10845-020-01614-w

Multi-view Kernel PCA for Time series Forecasting

Autori: Pandey, A; De Meulemeester, H; De Moor, Bart; Suykens, J
Pubblicato in: Neurocomputing, Numero 2023; Vol. 554, 2023, ISSN 0925-2312
Editore: Elsevier BV
DOI: 10.1016/j.neucom.2023.126639

Transfer Learning in Demand Response: a Review of Algorithms for Data-efficient Modelling and Control

Autori: Peirelinck, Thijs; Kazmi, Hussain Syed; Mbuwir, Brida; Hermans, Chris; Spiessens, Fred; Suykens, Johan; Deconinck, Geert
Pubblicato in: Energy and AI, Numero 2022; Vol. 7, 2022, ISSN 2666-5468
Editore: Elsevier
DOI: 10.1016/j.egyai.2021.100126

Toward Deep Adaptive Hinging Hyperplanes

Autori: Qinghua Tao, Jun Xu, Zhen Li, Na Xie, Shuning Wang, Xiaoli Li, Johan A. K. Suykens
Pubblicato in: IEEE Transactions on Neural Networks and Learning Systems, 2021, Pagina/e 1-15, ISSN 2162-237X
Editore: IEEE Computational Intelligence Society
DOI: 10.1109/tnnls.2021.3079113

Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer

Autori: Chen, Y; Shen, X; Liu, Y; Tao, Q; Suykens, Johan
Pubblicato in: Pattern Recognition Letters, 2023, ISSN 0167-8655
Editore: Elsevier BV
DOI: 10.1016/j.patrec.2022.12.023

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

Autori: Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens
Pubblicato in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, Pagina/e 1-1, ISSN 0162-8828
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tpami.2021.3097011

Explainable deep convolutional learning for intuitive model development by non–machine learning domain experts

Autori: Sundaravelpandian Singaravel, Johan Suykens, Hans Janssen, Philipp Geyer
Pubblicato in: Design Science, Numero 6, 2020, ISSN 2053-4701
Editore: DSJ
DOI: 10.1017/dsj.2020.22

Positive semi-definite embedding for dimensionality reduction andout-of-sample extensions

Autori: Fanuel M., Aspeel A., Delvenne J.C., Suykens J.A.K.,
Pubblicato in: SIAM Journal on Mathematics of Data Science, Numero Article accepted for publication, 2021, ISSN 2577-0187
Editore: Society for Industrial and Applied Mathematics
DOI: 10.1137/20m1370653

Island Transpeciation: A Co-Evolutionary Neural Architecture Search, applied to country-scale air-quality forecasting

Autori: Theodorakos, Konstantinos; Agudelo Manozca, Oscar; Schreurs, J; Suykens, J; De Moor, Bart
Pubblicato in: Ieee Transactions On Evolutionary Computation, 2023, ISSN 1089-778X
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tevc.2022.3189500

Nyström landmark sampling and regularized Christoffel functions

Autori: Michaël Fanuel; Joachim Schreurs; Johan A. K. Suykens
Pubblicato in: Machine Learning;, Numero 2022; Vol. 111; iss. 6, 2022, Pagina/e 2213 - 2254, ISSN 0885-6125
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10994-022-06165-0

Generalization Properties of hyper-RKHS and its Applications

Autori: Liu, Fanghui ; Shi, Lei ; Huang, Xiaolin ; Yang, Jie ; Suykens, Johan AK
Pubblicato in: Journal Of Machine Learning Research; 2021; Vol. 22; pp. -, 2021, ISSN 1532-4435
Editore: MIT Press

Towards a Unified Quadrature Framework for Large-Scale Kernel Machines

Autori: Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens
Pubblicato in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, Pagina/e 1-1, ISSN 0162-8828
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tpami.2021.3120183

Analysis of regularized least-squares in reproducing kernel Kreĭn spaces

Autori: Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens
Pubblicato in: Machine Learning, 2021, ISSN 0885-6125
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10994-021-05955-2

Generative Restricted Kernel Machines: A framework for multi-view generation and disentangled feature learning

Autori: Arun Pandey, Joachim Schreurs, Johan A.K. Suykens
Pubblicato in: Neural Networks, Numero 135, 2021, Pagina/e 177-191, ISSN 0893-6080
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.neunet.2020.12.010

Denoising modulo samples: k-NN regression and tightness of SDP relaxation

Autori: Fanuel, Michaël; Tyagi, Hemant
Pubblicato in: Information and Inference, Oxford University Press (OUP), 2021, Numero 4, 2021, ISSN 2049-8772
Editore: Institute of Mathematics and its Applications
DOI: 10.1093/imaiai/iaab022

Learning with continuous piecewise linear decision trees

Autori: Qinghua Tao, Zhen Li, Jun Xu, Na Xie, Shuning Wang, Johan A.K. Suykens
Pubblicato in: Expert Systems with Applications, Numero 168, 2021, Pagina/e 114214, ISSN 0957-4174
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.eswa.2020.114214

A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels

Autori: Fanghui Liu, Xiaolin Huang, Lei Shi, Jie Yang, Johan A. K. Suykens
Pubblicato in: IEEE Transactions on Neural Networks and Learning Systems, Numero 31/8, 2020, Pagina/e 2965-2979, ISSN 2162-237X
Editore: IEEE Computational Intelligence Society
DOI: 10.1109/tnnls.2019.2934729

Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

Autori: Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan Suykens
Pubblicato in: Proceedings of the AAAI Conference on Artificial Intelligence, Numero 34/04, 2020, Pagina/e 4844-4851, ISSN 2374-3468
Editore: AAAI Press
DOI: 10.1609/aaai.v34i04.5920

Outlier detection in non-elliptical data by kernel MRCD

Autori: Joachim Schreurs, Iwein Vranckx, Mia Hubert, Johan A. K. Suykens, Peter J. Rousseeuw
Pubblicato in: Statistics and Computing, Numero 31/5, 2021, ISSN 0960-3174
Editore: Kluwer Academic Publishers
DOI: 10.1007/s11222-021-10041-7

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

Autori: Francesco Tonin, Panagiotis Patrinos, Johan A.K. Suykens
Pubblicato in: Neural Networks, Numero 142, 2021, Pagina/e 661-679, ISSN 0893-6080
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.neunet.2021.07.023

Generalized support vector regression: Duality and tensor-kernel representation

Autori: Saverio Salzo, Johan A. K. Suykens
Pubblicato in: Analysis and Applications, Numero 18/01, 2020, Pagina/e 149-183, ISSN 0219-5305
Editore: World Scientific
DOI: 10.1142/s0219530519410069

Deformed Laplacians and spectral ranking in directed networks

Autori: M. Fanuel, J.A.K. Suykens
Pubblicato in: Applied and Computational Harmonic Analysis, Numero 47/2, 2019, Pagina/e 397-422, ISSN 1063-5203
Editore: Academic Press
DOI: 10.1016/j.acha.2017.09.002

Sparse Kernel Regression with Coefficient-based ℓq− regularization

Autori: Lei Shi, Xiaolin Huang, Yunlong Feng, Johan A.K. Suykens
Pubblicato in: Journal of Machine Learning Research, Numero 20(161), 2019, Pagina/e 1-44, ISSN 1532-4435
Editore: MIT Press

Robust classification of graph-based data

Autori: Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens
Pubblicato in: Data Mining and Knowledge Discovery, Numero 33/1, 2019, Pagina/e 230-251, ISSN 1384-5810
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10618-018-0603-9

Multi-View Kernel Spectral Clustering

Autori: Lynn Houthuys, Rocco Langone, Johan A.K. Suykens
Pubblicato in: Information Fusion, Numero 44, 2018, Pagina/e 46-56, ISSN 1566-2535
Editore: Elsevier BV
DOI: 10.1016/j.inffus.2017.12.002

Parallelized Tensor Train Learning of Polynomial Classifiers

Autori: Zhongming Chen, Kim Batselier, Johan A. K. Suykens, Ngai Wong
Pubblicato in: IEEE Transactions on Neural Networks and Learning Systems, Numero 29/10, 2018, Pagina/e 4621-4632, ISSN 2162-237X
Editore: IEEE Computational Intelligence Society
DOI: 10.1109/tnnls.2017.2771264

A Statistical Learning Approach to Modal Regression

Autori: Yunlong Feng, Jun Fan, Johan A.K. Suykens
Pubblicato in: Journal of Machine Learning Research, Numero 21(2), 2020, Pagina/e 1-35, ISSN 1532-4435
Editore: MIT Press

Modified Frank–Wolfe algorithm for enhanced sparsity in support vector machine classifiers

Autori: Carlos M. Alaíz, Johan A.K. Suykens
Pubblicato in: Neurocomputing, Numero 320, 2018, Pagina/e 47-59, ISSN 0925-2312
Editore: Elsevier BV
DOI: 10.1016/j.neucom.2018.08.049

Transductive LSTM for time-series prediction: An application to weather forecasting

Autori: Zahra Karevan, Johan A.K. Suykens
Pubblicato in: Neural Networks, Numero 125, 2020, Pagina/e 1-9, ISSN 0893-6080
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.neunet.2019.12.030

Deep convolutional learning for general early design stage prediction models

Autori: Sundaravelpandian Singaravel, Johan Suykens, Philipp Geyer
Pubblicato in: Advanced Engineering Informatics, Numero 42, 2019, Pagina/e 100982, ISSN 1474-0346
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.aei.2019.100982

Disentangled Representation Learning and Generation With Manifold Optimization

Autori: Arun, Pandey; Michaël, Fanuel; Joachim, Schreurs; Johan A K, Suykens
Pubblicato in: Neural Computation, Numero Vol. 34; iss. 10; pp. 2009 - 2036, 2022, ISSN 0899-7667
Editore: MIT Press
DOI: 10.1162/neco_a_01528

Diversity Sampling is an Implicit Regularization for Kernel Methods

Autori: Michael Fanuel, Joachim Schreurs, Johan Suykens
Pubblicato in: SIAM Journal on Mathematics of Data Science, Numero 3/1, 2021, Pagina/e 280-297, ISSN 2577-0187
Editore: SIAM
DOI: 10.1137/20m1320031

A novel neural grey system model with Bayesian regularization and its applications

Autori: Xin Ma; Mei Xie; Johan A.K. Suykens
Pubblicato in: Elsevier Neurocomputing, Numero 2, 2021, ISSN 0925-2312
Editore: Elsevier BV
DOI: 10.1016/j.neucom.2021.05.048

Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems

Autori: Michaël Fanuel; Joachim Schreurs; Johan A. K. Suykens
Pubblicato in: SIAM journal on Mathematics of Data Science, Numero 1, 2022, ISSN 2577-0187
Editore: SIAM
DOI: 10.1137/21m1403977

Piecewise Linear Neural Networks and Deep Learning

Autori: Tao, Q; Li, L; Huang, X; Wang, S; Suykens, Johan
Pubblicato in: Nature Reviews Methods Primers, Numero 2022; Vol. 2; iss 1, 2022, ISSN 2662-8449
Editore: Nature
DOI: 10.1038/s43586-022-00125-7

Compressing Features for Learning with Noisy Labels

Autori: Yingyi Chen; Shell Xu Hu; Xi Shen; Chunrong Ai; Johan A. K. Suykens
Pubblicato in: Crossref, Numero 5, 2022, ISSN 2162-237X
Editore: IEEE Computational Intelligence Society
DOI: 10.48550/arxiv.2206.13140

Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems

Autori: Michaël Fanuel, Joachim Schreurs, Johan A.K. Suykens
Pubblicato in: SIAM journal on Mathematics of Data Science, Numero 4 (3), 1171-1190, 2022, ISSN 2577-0187
Editore: SIAM
DOI: 10.48550/arxiv.2011.06964

Learning with asymmetric kernels : Least squares and feature interpretation

Autori: He, M; He, F; Shi, L; Huang, X; Suykens, Johan
Pubblicato in: Ieee Transactions On Pattern Analysis And Machine Intelligence, 2023, ISSN 0162-8828
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tpami.2023.3257351

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

Autori: Tao, Qinghua; Li, Z; Xu, J; Lin, S; De Schutter, Bart; Suykens, J
Pubblicato in: Ieee Transactions On Intelligent Transportation Systems; 2022, Numero Vol. 23; iss. 9, 2022, ISSN 1524-9050
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tits.2022.3142728

Deep kernel principal component analysis for multi-level feature learning

Autori: Tonin, F; Tao, Q; Patrinos, Panagiotis; Suykens, J
Pubblicato in: Neural Networks, Numero 2024; Vol. 170; pp. 578 - 595, 2024, ISSN 0893-6080
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.neunet.2023.11.045

Efficient hinging hyperplanes neural network and its application in nonlinear system identification

Autori: Jun Xu, Qinghua Tao, Zhen Li, Xiangming Xi, Johan A.K. Suykens, Shuning Wang
Pubblicato in: Automatica, Numero 116, 2020, Pagina/e 108906, ISSN 0005-1098
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.automatica.2020.108906

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