mlsanityData to 14 Oct 2026

A Comparison of the Delta Method and the Bootstrap in Deep Learning Classification

arXiv 2107.01606 ↗First posted 4 Jul 2021cs.LG stat.MLcited by 0

Not on the map. Why?

Abstract (arXiv)

We validate the recently introduced deep learning classification adapted Delta method by a comparison with the classical Bootstrap. We show that there is a strong linear relationship between the quantified predictive epistemic uncertainty levels obtained from the two methods when applied on two LeNet-based neural network classifiers using the MNIST and CIFAR-10 datasets. Furthermore, we demonstrate that the Delta method offers a five times computation time reduction compared to the Bootstrap.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 14 references that are in the map. First posted on arXiv 4 Jul 2021.

Builds on

6 of 14 references.

References · 6

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  3. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
  4. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 913
  5. 2016Deep Exploration via Bootstrapped DQNcited by 527
  6. 2018Deterministic Implementations for Reproducibility in Deep Reinforcement Learningcited by 19

Built on by

Not cited yet.