1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CV2024
Momentum Auxiliary Network for Supervised Local Learning
Junhao Su, Changpeng Cai, Feiyu Zhu +4
Deep neural networks conventionally employ end-to-end backpropagation for their training process, which lacks biological credibility and triggers a locking dilemma during network p…
cs.CV2024
HPFF: Hierarchical Locally Supervised Learning with Patch Feature Fusion
Junhao Su, Chenghao He, Feiyu Zhu +3
Traditional deep learning relies on end-to-end backpropagation for training, but it suffers from drawbacks such as high memory consumption and not aligning with biological neural n…
cs.NE2024★ 1 cited
Scaling Supervised Local Learning with Augmented Auxiliary Networks
Chenxiang Ma, Jibin Wu, Chenyang Si +1
Deep neural networks are typically trained using global error signals that backpropagate (BP) end-to-end, which is not only biologically implausible but also suffers from the updat…