
This paper proposes a novel analogy between the dynamics of feature learning in deep neural networks (DNNs) and the behavior of a spring-block chain, providing a macroscopic perspective on how factors like nonlinearity and noise influence feature learning across layers.


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a-spring-block-analogy-for-understanding-feature-learning-dynamics-in-deep-neural-networks


A Spring-Block Analogy for Understanding Feature Learning Dynamics in Deep Neural Networks


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Spurious features impact core feature learning dynamics in neural networks.


feature-learning-dynamics-in-neural-networks-with-spurious-correlations


Feature Learning Dynamics in Neural Networks with Spurious Correlations



The author explores the impact of spurious features on core feature learning dynamics in neural networks, revealing insights into the complexity and correlation strength. By analyzing theoretical frameworks and empirical findings, the study sheds light on the challenges and implications of spurious correlations in training neural networks.


understanding-feature-learning-dynamics-in-neural-networks-with-spurious-correlations


Understanding Feature Learning Dynamics in Neural Networks with Spurious Correlations
