张英韬

博士研究生

研究方向

稀疏训练、修剪和量化

论文发表

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.