撞上LeCun新研究！清华团队让神经网络“简单性”可测可优化
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TL;DR - A Tsinghua team proposes Effective Degree (ED), a differentiable measure of neural-network simplicity based on polynomial approximations along data interpolation paths. ED strongly tracks generalization and can regularize training across vision, language, vision-language, and reinforcement-learning tasks.
- ED measures function complexity by weighting Chebyshev polynomial components according to their degree and magnitude.
- On CIFAR-10 ResNet18 experiments, ED correlated with the generalization gap at 0.99, outperforming parameter norms and sharpness metrics.
- Because polynomial fitting is differentiable, ED can be added directly to training objectives to discourage unnecessary high-order behavior.
- ED does not ensure causal or robust features: models may still favor simple but unreliable shortcuts.
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撞上LeCun新研究！清华团队让神经网络“简单性”可测可优化
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TL;DR - A Tsinghua team proposes Effective Degree (ED), a differentiable measure of neural-network simplicity based on polynomial approximations along data interpolation paths. ED strongly tracks generalization and can regularize training across vision, language, vision-language, and reinforcement-learning tasks.
- ED measures function complexity by weighting Chebyshev polynomial components according to their degree and magnitude.
- On CIFAR-10 ResNet18 experiments, ED correlated with the generalization gap at 0.99, outperforming parameter norms and sharpness metrics.
- Because polynomial fitting is differentiable, ED can be added directly to training objectives to discourage unnecessary high-order behavior.
- ED does not ensure causal or robust features: models may still favor simple but unreliable shortcuts.