[ICLR 2023] "More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity"; [ICML 2023] "Are Large Kernels Better Teachers than Transformers for ConvNets?"

[NeurIPS'21] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang

Event-driven Computation for Brain Dynamics.

This project implements a self-pruning neural network for CIFAR-10 classification, where learnable gate parameters enable dynamic removal of less important weights during training. Using L1 regularization, the model achieves sparsity while maintaining competitive accuracy, improving efficiency.