Brain data is worth a variable number of task samples depending on task-brain alignment, noise levels, and latent dimension, with conditions under which it also improves robustness to test distribution shift.
Double descent demystified: Identifying, interpreting & ablating the sources of a deep learning puzzle
3 Pith papers cite this work. Polarity classification is still indexing.
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A nonasymptotic generalization error upper bound for path-regularized multilayer neural networks with Lipschitz losses that exhibits double descent and is near-minimax optimal for ReLU regression.
Quantum kernel ridge regression shows double descent in test risk, with the interpolation peak suppressible by regularization, via random matrix theory asymptotics in the high-dimensional limit.
citing papers explorer
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How Much is Brain Data Worth for Machine Learning?
Brain data is worth a variable number of task samples depending on task-brain alignment, noise levels, and latent dimension, with conditions under which it also improves robustness to test distribution shift.
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Path Regularization: A Near-Complete and Optimal Nonasymptotic Generalization Theory for Multilayer Neural Networks and Double Descent Phenomenon
A nonasymptotic generalization error upper bound for path-regularized multilayer neural networks with Lipschitz losses that exhibits double descent and is near-minimax optimal for ReLU regression.
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Double Descent in Quantum Kernel Ridge Regression
Quantum kernel ridge regression shows double descent in test risk, with the interpolation peak suppressible by regularization, via random matrix theory asymptotics in the high-dimensional limit.