Search Results for author:
Found 2 papers, 1 papers with code
SDiT: Spiking Diffusion Model with Transformer
In this paper, we explore a novel diffusion model architecture within spiking neural networks.
Go beyond End-to-End Training: Boosting Greedy Local Learning with Context Supply
Traditional end-to-end (E2E) training of deep networks necessitates storing intermediate activations for back-propagation, resulting in a large memory footprint on GPUs and restricted model parallelization.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
