| Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data F Ye PloS one 12 (12), e0188746, 2017 | 78 | 2017 |
| Learning latent representations across multiple data domains using lifelong VAEGAN F Ye, AG Bors Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 43 | 2020 |
| An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications F Ye, XY Lou, LF Sun PLoS One 12 (4), e0173516, 2017 | 29 | 2017 |
| Evolving the SVM model based on a hybrid method using swarm optimization techniques in combination with a genetic algorithm for medical diagnosis F Ye Multimedia Tools and Applications 77 (3), 3889-3918, 2018 | 28 | 2018 |
| Lifelong teacher-student network learning F Ye, AG Bors IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (10), 6280 …, 2021 | 22 | 2021 |
| Dense adaptive cascade forest: a self-adaptive deep ensemble for classification problems H Wang, Y Tang, Z Jia, F Ye Soft Computing 24 (4), 2955-2968, 2020 | 19 | 2020 |
| Lifelong mixture of variational autoencoders F Ye, AG Bors IEEE Transactions on Neural Networks and Learning Systems, 2021 | 18 | 2021 |
| Learning joint latent representations based on information maximization F Ye, AG Bors Information Sciences 567, 216-236, 2021 | 18 | 2021 |
| Deep mixture generative autoencoders F Ye, AG Bors IEEE Transactions on Neural Networks and Learning Systems 33 (10), 5789-5803, 2021 | 18 | 2021 |
| Simultaneous Support Vector selection and parameter optimization using Support Vector Machines for sentiment classification Y Fei 2016 7th IEEE International Conference on Software Engineering and Service …, 2016 | 18 | 2016 |
| Lifelong infinite mixture model based on knowledge-driven Dirichlet process F Ye, AG Bors Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 16 | 2021 |
| Lifelong learning of interpretable image representations F Ye, AG Bors 2020 Tenth International Conference on Image Processing Theory, Tools and …, 2020 | 14 | 2020 |
| Mixtures of variational autoencoders F Ye, AG Bors 2020 Tenth International Conference on Image Processing Theory, Tools and …, 2020 | 14 | 2020 |
| Lifelong twin generative adversarial networks F Ye, AG Bors 2021 IEEE International Conference on Image Processing (ICIP), 1289-1293, 2021 | 13 | 2021 |
| Simultaneous feature with support vector selection and parameters optimization using GA-based SVM solve the binary classification Y Fei, H Min 2016 First IEEE International Conference on Computer Communication and the …, 2016 | 13 | 2016 |
| InfoVAEGAN: Learning joint interpretable representations by information maximization and maximum likelihood F Ye, AG Bors 2021 IEEE International Conference on Image Processing (ICIP), 749-753, 2021 | 11 | 2021 |
| Lifelong generative modelling using dynamic expansion graph model F Ye, AG Bors Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8857-8865, 2022 | 9 | 2022 |
| Continual variational autoencoder learning via online cooperative memorization F Ye, AG Bors Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel …, 2022 | 8 | 2022 |
| Task-Free Continual Learning via Online Discrepancy Distance Learning F Ye, AG Bors arXiv preprint arXiv:2210.06579, 2022 | 8 | 2022 |
| Learning an evolved mixture model for task-free continual learning F Ye, AG Bors 2022 IEEE International Conference on Image Processing (ICIP), 1936-1940, 2022 | 6 | 2022 |
Adrian G. BorsDepartment of Computer Science, University of YorkGeverifieerd e-mailadres voor york.ac.uk
