Proposes pointwise Riemannian Dimension from feature eigenvalues to derive tighter, representation-aware generalization bounds for deep networks in the nonlinear regime.
arXiv preprint arXiv:1902.04742 , year =
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Derives an upper bound on frozen LM expected risk from proxy risk, SAE reconstruction gap, concept-pool mismatch and sparse complexity, with non-vacuous bounds observed on GPT-2, Gemma-2B and Llama-3-8B.
Review of neural scaling laws and their relation to constraints and inductive biases when applying machine learning to physics problems.
citing papers explorer
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Pointwise Generalization in Deep Neural Networks
Proposes pointwise Riemannian Dimension from feature eigenvalues to derive tighter, representation-aware generalization bounds for deep networks in the nonlinear regime.
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From Sparse Features to Trustworthy Proxies: Certifying SAE-Based Interpretability
Derives an upper bound on frozen LM expected risk from proxy risk, SAE reconstruction gap, concept-pool mismatch and sparse complexity, with non-vacuous bounds observed on GPT-2, Gemma-2B and Llama-3-8B.