Faculty Profiles

Li, X., Sakevych, M., Atkinson, G., & Metsis, V. (2024). Biodiffusion: A versatile diffusion model for biomedical signal synthesis. Bioengineering, 11(4). https://doi.org/10.3390/bioengineering11040299

Li, X., Nabati, R., Singh, K., Corona, E., Metsis, V., & Parchami, A. (2023). EMOD: Efficient Moving Object Detection via Image Eccentricity Analysis and Sparse Neural Networks. In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops (pp. 51–59). IEEE Xplore. https://doi.org/https://doi.org/10.1109/WACVW58289.2023.00010

Atkinson, G. M., Li, X., & Metsis, V. (2023). Conditional Diffusion with Label Smoothing for Data Synthesis from Examples with Noisy Labels. In 31st European Signal Processing Conference (EUSIPCO 2023) (pp. 1300–1304). IEEE. https://doi.org/10.23919/EUSIPCO58844.2023.10289794

Li, X., Metsis, V., & Ngu, A. H. (2023). Generating Realistic Multi-class Biosignals with BioSGAN: A Transformer-based Label-guided Generative Adversarial Network. In The 25th International Conference on Artificial Intelligence (ICAI’23). USA: IEEE.

Li, X., Metsis, V., Wang, H., & Ngu, H. H. (2022). TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network. https://doi.org/0.1007/978-3-031-09342-5_13

Li, X., & Metsis, V. (2022). SPP-EEGNET: An Input-Agnostic Self-supervised EEG Representation Model for Inter-dataset Transfer Learning. In 18th International Conference on Computing and Information Technology (IC2IT 2022). Springer. https://doi.org/https://doi.org/10.1007/978-3-030-99948-3_17

Blakeney, C. J., Li, X., Yan, Y., & Zong, Z. (2021). Parallel Blockwise Knowledge Distillation for Deep Neural Network Compression. IEEE Transactions on Parallel and Distributed Computing (TPDS), 32, 1765–1776.

Blakeney, C., Li, X., Yan Yan, & Zong, Z. (2020). Craft Distillation: Layer-wise Training and Replacement Model Distillation.

Li, X., Blakeney, C. J., & Zong, Z. (2020). Transfer Learning with Fine-grained Sparse Networks: From an Efficient Network Perspective.