EleutherAI researchers are investigating the inductive biases of random neural networks by analyzing the volume of local function spaces. Their work builds upon previous studies to understand how properties at network initialization might predict generalization behavior during training. The research hypothesizes that popular architectures inherently favor simpler functions, and that complexity increases with training, potentially leading to shortcut learning if simplicity bias is too extreme. AI
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RANK_REASON This is a research paper analyzing theoretical properties of neural networks.
