@inproceedings{
ren2024towards,
title={Towards the Dynamics of a {DNN} Learning Symbolic Interactions},
author={Qihan Ren and Junpeng Zhang and Yang Xu and Yue Xin and Dongrui Liu and Quanshi Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dIHXwKjXRE}
}
This study proves the two-phase dynamics of a deep neural network (DNN) learning interactions. Despite the long disappointing view of the faithfulness of post-hoc explanation of a DNN, a series of theorems have been proven [27] in recent years to show that for a given input sample, a small set of interactions between input variables can be considered as primitive inference patterns that faithfully represent a DNN's detailed inference logic on that sample. Particularly, Zhang et al. [41] have observed that various DNNs all learn interactions of different complexities in two distinct phases, and this two-phase dynamics well explains how a DNN changes from under-fitting to over-fitting. Therefore, in this study, we mathematically prove the two-phase dynamics of interactions, providing a theoretical mechanism for how the generalization power of a DNN changes during the training process. Experiments show that our theory well predicts the real dynamics of interactions on different DNNs trained for various tasks.
Towards the Dynamics of a DNN Learning Symbolic Interactions
Qihan Ren Junpeng Zhang Yang Xu Yue Xin Dongrui Liu Quanshi Zhang