New research challenges Fourier Alignment in single-neuron AI models

Researchers have presented a counterexample to the Fourier Alignment hypothesis in single-neuron modular addition, demonstrating a scenario where a ReLU neuron can become inactive over time. This failure mode occurs with positive probability under Gaussian initialization and can be extended to various Clarke trajectories and approximations of ReLU. The findings suggest that single-frequency alignment is not a general outcome when training a single neuron on modular addition tasks. AI

IMPACT Challenges assumptions about neuron behavior during training, potentially impacting future neural network architectures.

RANK_REASON The cluster contains a peer-reviewed academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.NE (Neural & Evolutionary) →

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