Welcome to my personal webset
FeI Ye
About me
My name is Fei Ye (叶飞). I am now a third-year PhD student, supervised by Dr.Adrian Bor, University of York, UK. My research mainly focuses on machine learning, lifelong learning and generative modelling.
My Google scholar is Fei Ye - Google Scholar
publication
-Ye, Fei, and Adrian G. Bors. "Dynamic Self-Supervised Teacher-Student Network Learning." IEEE Transactions on Pattern Analysis & Machine Intelligence 01 (2022): 1-19.
-Ye Fei, and Adrian Bors, "Continual Variational Autoencoder Learning via Online Cooperative Memorization
", ECCV 2022
-Ye Fei, and Adrian Bors, "Task-Free Continual Learning via Online Discrepancy Distance Learning", NeurIPS 2022 (Spotlight)
-Ye, Fei, and Adrian Bors. "Lifelong Teacher-Student Network Learning." IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).
Ye, Fei, and Adrian G. Bors. , Lifelong Infinite Mixture Model Based on Knowledge-Driven Dirichlet Process International Conference on Computer Vision 2021 (ICCV 2021)
Ye, Fei, and Adrian G. Bors. , Lifelong Generative Modelling Using Dynamic Expansion Graph Model (AAAI 2022, Oral )
-Ye, Fei, and Adrian G. Bors. "Learning joint latent representations based on information maximization." Information Sciences 567 (2021): 216-236.
- Ye, Fei, and Adrian G. Bors. "Deep mixture generative autoencoders." IEEE Transactions on Neural Networks and Learning Systems (2021).
- Ye, Fei, and Adrian G. Bors. "Lifelong Mixture of Variational Autoencoders." IEEE Transactions on Neural Networks and Learning Systems (2021).
-Ye, Fei, and Adrian G. Bors. "Learning latent representations across multiple data domains using Lifelong VAEGAN." European Conference on Computer Vision. Springer, Cham, 2020.
-Ye, Fei, and Adrian G. Bors. "Lifelong learning of interpretable image representations." 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2020.
-Ye, Fei, and Adrian G. Bors. "Mixtures of variational autoencoders." 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2020.
-Wang, Haiyang, et al. "Dense adaptive cascade forest: a self-adaptive deep ensemble for classification problems." Soft Computing 24.4 (2020): 2955-2968.
-Ye, Fei. "Evolving the SVM model based on a hybrid method using swarm optimization techniques in combination with a genetic algorithm for medical diagnosis." Multimedia Tools and Applications 77.3 (2018): 3889-3918.
-Ye, Fei. "Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data." PloS one 12.12 (2017): e0188746.
-Ye, Fei, Xin Yuan Lou, and Lin Fu Sun. "An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications." PloS one 12.4 (2017): e0173516.