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1,460 claims from 908 papers are on the record. 46 have been checked so far; the other 1,414 have no check with a result yet.

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Keyword: inductive bias ×Clear all

7 claims from 4 papers

1. Computer Science › Advanced Neural Network Applications

   One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers

   Morcos, Yu, Paganini and Tian · arXiv (Cornell University) · 2019

   The paper tests whether sparse 'winning ticket' network initializations found for one dataset and optimizer work on others, and reports that they generalise across natural-image datasets and optimizers.

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   1. UncheckedWinning tickets found on larger datasets consistently transferred better to other datasets than those found on smaller ones, within natural images.“Moreover, winning tickets generated using larger datasets consistently transferred better than those generated using smaller datasets.”

2. Computer Science › Neural Networks and Applications

   Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks

   Canatar, Bordelon and Pehlevan · Nature Communications · 2021

   The paper derives a statistical-mechanics formula for generalisation error in kernel regression, which also covers infinitely wide neural networks, and uses it to explain which tasks suit a given kernel.

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   1. Unchecked“We elucidate an inductive bias of kernel regression to explain data with "simple functions", which are identified by solving a kernel eigenfunction problem on the data distribution.”
   2. UncheckedIn kernel regression, adding more data may worsen generalisation when data are noisy or not expressible by the kernel, giving learning curves with several peaks.“We show that more data may impair generalization when noisy or not expressible by the kernel, leading to non-monotonic learning curves with possibly many peaks.”

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.”

4. Computer Science › Advanced Neural Network Applications

   A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications

   Cao and Wu · arXiv (Cornell University) · 2021

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   1. Unchecked“Experimental results show that the proposed Tobias significantly improves downstream tasks, especially for object detection.”
   2. Unchecked“This paper also shows that Tobias has consistent improvements on training sets of different sizes, and is more resilient to changes in image augmentations.”