Summary of Towards the Dynamics Of a Dnn Learning Symbolic Interactions, by Qihan Ren et al.
Towards the Dynamics of a DNN Learning Symbolic Interactions
by Qihan Ren, Junpeng Zhang, Yang Xu, Yue Xin, Dongrui Liu, Quanshi Zhang
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study demonstrates the two-phase dynamics of deep neural network (DNN) learning interactions, shedding light on how a DNN’s generalization power changes during training. Building upon recent theorems showing that primitive inference patterns can faithfully represent a DNN’s detailed inference logic, this research mathematically proves the two-phase dynamics of interactions. This dynamic is characterized by two distinct phases, where a DNN initially learns simple interactions before transitioning to more complex ones, eventually leading to over-fitting. The authors’ theory well predicts real-world dynamics on various DNNs trained for different tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study shows that deep neural networks (DNNs) learn in two stages. First, they learn simple patterns, and then they get better at recognizing more complex ones. This helps us understand why DNNs sometimes don’t generalize well to new data. The researchers used math to prove this idea, which matches what happens when different DNNs are trained for various tasks. |
Keywords
» Artificial intelligence » Generalization » Inference » Neural network
