Depth induces an implicit low-rank bias in deep unconstrained feature models trained with unregularized multiclass cross-entropy, promoting softmax codes over neural collapse via more efficient norm propagation.
arXiv preprint arXiv:2206.05794 , year=
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LLM-driven evolutionary search discovers unsupervised UQ methods as Python programs that improve ROC-AUC by up to 6.7% over manual baselines on atomic claim verification across 9 datasets with OOD generalization.
Weight decay controls distinct learning regimes in grokking transformers on modular arithmetic, tracked by new cheap attention-based diagnostics with empirical critical value and exponent fits.
Weight decay slows progressive sharpening at the edge of stability, inducing damped oscillations in CNNs and a phase transition to sub-2/η sharpness in MLPs driven by parameter-sharpness gradient alignment, yielding more stable NTK dynamics.
citing papers explorer
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The Implicit Bias of Depth: From Neural Collapse to Softmax Codes
Depth induces an implicit low-rank bias in deep unconstrained feature models trained with unregularized multiclass cross-entropy, promoting softmax codes over neural collapse via more efficient norm propagation.
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Evolutionary Search for Automated Design of Uncertainty Quantification Methods
LLM-driven evolutionary search discovers unsupervised UQ methods as Python programs that improve ROC-AUC by up to 6.7% over manual baselines on atomic claim verification across 9 datasets with OOD generalization.
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Weight Decay Regimes in Grokking Transformers: Cheap Online Diagnostics
Weight decay controls distinct learning regimes in grokking transformers on modular arithmetic, tracked by new cheap attention-based diagnostics with empirical critical value and exponent fits.
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Does Weight Decay Enhance Training Stability?
Weight decay slows progressive sharpening at the edge of stability, inducing damped oscillations in CNNs and a phase transition to sub-2/η sharpness in MLPs driven by parameter-sharpness gradient alignment, yielding more stable NTK dynamics.