Towards the Dynamics of a DNN Learning Symbolic Interactions

• URL: http://arxiv.org/abs/2407.19198v2
• Date: Mon, 25 Nov 2024 08:57:20 GMT
• Title: Towards the Dynamics of a DNN Learning Symbolic Interactions
• Authors: Qihan Ren, Junpeng Zhang, Yang Xu, Yue Xin, Dongrui Liu, Quanshi Zhang,
• Abstract summary: A series of theorems have been proven 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. This study proves the two-phase dynamics of interactions, providing a theoretical mechanism for how the generalization power of a DNN changes during the training process.
• Score: 20.493304123269446
• License:
• Abstract: 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 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. 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.

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