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1,353 claims from 842 papers are on the record. 46 have been checked so far; the other 1,307 have no check with a result yet.

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Keyword: overparameterized models ×Clear all

5 claims from 3 papers

1. Computer Science › Advanced Neural Network Applications

   Rethinking the Value of Network Pruning

   Liu, Sun, Zhou, Huang and Darrell · arXiv (Cornell University) · 2018

   The paper tests common pruning pipelines and reports that training the pruned architecture from scratch matches or beats fine-tuning, suggesting the architecture matters more than inherited weights.

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   1. UncheckedIn the structured pruning methods examined, fine-tuning a pruned network performed no better than training the same small network from random starting weights.“For all state-of-the-art structured pruning algorithms we examined, fine-tuning a pruned model only gives comparable or worse performance than training that model with randomly initialized weights.”

2. Computer Science › Stochastic Gradient Optimization Techniques

   Memorizing without overfitting: Bias, variance, and interpolation in overparameterized models

   Rocks and Mehta · Physical Review Research · 2022

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   1. Unchecked“In both models, increasing the number of fit parameters leads to a phase transition where the training error goes to zero and the test error diverges as a result of the variance (while the bias remains finite).”
   2. Unchecked“We also show that in contrast with classical intuition, over-parameterized models can overfit even in the absence of noise and exhibit bias even if the student and teacher models match.”

3. Computer Science › Stochastic Gradient Optimization Techniques

   Multiple Descent: Design Your Own Generalization Curve

   Chen, Min, Belkin and Karbasi · arXiv (Cornell University) · 2020

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   1. Unchecked“We show that the generalization curve can have an arbitrary number of peaks, and moreover, locations of those peaks can be explicitly controlled.”
   2. Unchecked“Our results highlight the fact that both classical U-shaped generalization curve and the recently observed double descent curve are not intrinsic properties of the model family.”