Hebbian and Gradient-based Plasticity Enables Robust Memory and Rapid Learning in RNNs

1 code implementation • 7 Feb 2023 • Yu Duan, Zhongfan Jia, Qian Li, Yi Zhong, Kaisheng Ma

Comparing different plasticity rules under the same framework shows that Hebbian plasticity is well-suited for several memory and associative learning tasks; however, it is outperformed by gradient-based plasticity on few-shot regression tasks which require the model to infer the underlying mapping.

Few-Shot Learning

AFEC: Active Forgetting of Negative Transfer in Continual Learning

1 code implementation • NeurIPS 2021 • Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, Yi Zhong

Without accessing to the old training samples, knowledge transfer from the old tasks to each new task is difficult to determine, which might be either positive or negative.

Continual Learning Transfer Learning

Exploring Frequency Domain Interpretation of Convolutional Neural Networks

no code implementations • 27 Nov 2019 • Zhongfan Jia, Chenglong Bao, Kaisheng Ma

To the best of our knowledge, there is no study on the interpretation of modern CNNs from the perspective of the frequency proportion of filters.