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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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Status: Unchecked ×Keyword: double descent ×Clear all

9 claims from 7 papers

1. Computer Science › Stochastic Gradient Optimization Techniques

   Deep double descent: where bigger models and more data hurt*

   Nakkiran, Kaplun, Bansal, Yang, Barak and Sutskever · Journal of Statistical Mechanics Theory and Experiment · 2021

   The paper shows that modern deep learning tasks display double descent, defines an effective model complexity to unify the effects, and identifies regimes where more training data hurts test performance.

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   1. UncheckedDouble descent, where test performance gets worse then better, is reported to occur as training epochs increase, not only as model size increases.“Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epochs.”

2. Computer Science › Stochastic Gradient Optimization Techniques

   Surprises in high-dimensional ridgeless least squares interpolation

   Hastie, A, Rosset and Tibshirani · The Annals of Statistics · 2022

   The paper studies minimum-norm ('ridgeless') interpolating least squares in high dimensions, under linear and random one-layer neural network feature models, and reproduces phenomena seen in large neural networks.

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   1. UncheckedIn ridgeless least squares with as many parameters as samples, the paper reproduces double descent of prediction risk and possible benefits of overparametrization.“We recover—in a precise quantitative way—several phenomena that have been observed in large-scale neural networks and kernel machines, including the “double descent” behavior of the prediction risk, and the potential benefits of overparametrization.”

3. Computer Science › Stochastic Gradient Optimization Techniques

   The generalization error of random features regression: Precise asymptotics and double descent curve

   Song and A · arXiv (Cornell University) · 2019

   The paper computes exact large-scale limits of test error for ridge regression on random features, a two-layer network with random first-layer weights, and links the results to the double descent curve.

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   1. UncheckedThe paper presents random features ridge regression as a solvable model that shows all features of double descent without assuming special misspecification structures.“This provides the first analytically tractable model that captures all the features of the double descent phenomenon without assuming ad hoc misspecification structures.”

4. Computer Science › Stochastic Gradient Optimization Techniques

   Optimal Regularization Can Mitigate Double Descent

   Nakkiran, Venkat, Kakade and Ma · arXiv (Cornell University) · 2020

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   1. Unchecked“Theoretically, we prove that for certain linear regression models with isotropic data distribution, optimally-tuned $\ell_2$ regularization achieves monotonic test performance as we grow either the sample size or the model size.”

5. 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.”

6. Computer Science › Stochastic Gradient Optimization Techniques

   Triple descent and the two kinds of overfitting: where and why do they appear?*

   d’Ascoli, Sagun and Biroli · Journal of Statistical Mechanics Theory and Experiment · 2021

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   1. Unchecked“We show that this peak is implicitly regularized by the nonlinearity, which is why it only becomes salient at high noise and is weakly affected by explicit regularization.”

7. Computer Science › Stochastic Gradient Optimization Techniques

   On the interplay between data structure and loss function in classification problems

   d’Ascoli, Gabrié, Sagun and Biroli · arXiv (Cornell University) · 2021

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   1. Unchecked“Using methods from statistical physics, we derive a precise asymptotic expression for the train and test error achieved by random feature models trained to classify such data, which is valid for any convex loss function.”
   2. Unchecked“We study in detail how the data structure affects the double descent curve, and show that in the over-parametrized regime, its impact is greater for logistic loss than for mean-squared loss: the easier the task, the wider the gap in performance at the advant…