| 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 | 83 | 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 | 65 | 2020 |
| Lifelong teacher-student network learning F Ye, AG Bors IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (10), 6280 …, 2021 | 37 | 2021 |
| 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 | 34 | 2017 |
| Lifelong mixture of variational autoencoders F Ye, AG Bors IEEE Transactions on Neural Networks and Learning Systems 34 (1), 461-474, 2021 | 30 | 2021 |
| Deep mixture generative autoencoders F Ye, AG Bors IEEE Transactions on Neural Networks and Learning Systems 33 (10), 5789-5803, 2021 | 28 | 2021 |
| 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 |
| Learning joint latent representations based on information maximization F Ye, AG Bors Information Sciences 567, 216-236, 2021 | 25 | 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 | 24 | 2020 |
| Task-free continual learning via online discrepancy distance learning F Ye, AG Bors Advances in Neural Information Processing Systems 35, 23675-23688, 2022 | 23 | 2022 |
| 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 | 21 | 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 | 19 | 2016 |
| Lifelong learning of interpretable image representations F Ye, AG Bors 2020 Tenth International Conference on Image Processing Theory, Tools and …, 2020 | 18 | 2020 |
| Lifelong twin generative adversarial networks F Ye, AG Bors 2021 IEEE International Conference on Image Processing (ICIP), 1289-1293, 2021 | 17 | 2021 |
| Continual variational autoencoder learning via online cooperative memorization F Ye, AG Bors European Conference on Computer Vision, 531-549, 2022 | 16 | 2022 |
| Mixtures of variational autoencoders F Ye, AG Bors 2020 Tenth International Conference on Image Processing Theory, Tools and …, 2020 | 16 | 2020 |
| Dynamic self-supervised teacher-student network learning F Ye, AG Bors IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (5), 5731-5748, 2022 | 15 | 2022 |
| 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 | 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 |
| 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 | 11 | 2022 |
Adrian G. BorsDepartment of Computer Science, University of YorkBestätigte E-Mail-Adresse bei york.ac.uk
