Yingtao Zhang

Ph.D student

Personal introduction

2022.9 until now, Ph.D in Computer Science, Tsinghua University.

2020.9-2022.6 MSc in Electronic Information Engineering, Wuhan University.

2014.9-2018.6 BEng in Printing Engineering, Wuhan University.

Research Direction

My research direction is sparse training, pruning, and quantization.

Publications

Zhang, Y., Bai, H., Lin, H., Zhao, J., Hou, L. and Cannistraci, C.V., 2024, May. Plug-and-play: An efficient post-training pruning method for large language models. In The Twelfth International Conference on Learning Representations.

Zhang, Y., Zhao, J., Wu, W., Muscoloni, A. and Cannistraci, C.V., 2024, May. Epitopological learning and Cannistraci-Hebb network shape intelligence brain-inspired theory for ultra-sparse advantage in deep learning. In The Twelfth International Conference on Learning Representations.

Zhang, Y., Zhao, J., Liao, Z., Wu, W., Michieli, U. and Cannistraci, C.V., 2024. Brain-Inspired Sparse Training in MLP and Transformers with Network Science Modeling via Cannistraci-Hebb Soft Rule.

Zhao, J., Zhang, Y., Li, X., Liu, H. and Cannistraci, C.V., 2024. Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks. arXiv preprint arXiv:2405.15481.