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Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in Neural Networks

arXiv 2309.04742 ↗First posted 9 Sep 2023stat.ML cs.LG cs.NAcited by 0

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Abstract (arXiv)

We consider the problem of performing Bayesian inference for logistic regression using appropriate extensions of the ensemble Kalman filter. Two interacting particle systems are proposed that sample from an approximate posterior and prove quantitative convergence rates of these interacting particle systems to their mean-field limit as the number of particles tends to infinity. Furthermore, we apply these techniques and examine their effectiveness as methods of Bayesian approximation for quantifying predictive uncertainty in neural networks.

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References · 16

  1. 2017On Calibration of Modern Neural Networkscited by 4,860
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  4. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  5. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
  6. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 813
  7. 2015Weight Uncertainty in Neural Networkscited by 554
  8. 2018Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problemcited by 323
  9. 2015Scalable Bayesian Optimization Using Deep Neural Networkscited by 284
  10. 2020Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networkscited by 227
  11. 2017Noisy Natural Gradient as Variational Inferencecited by 94
  12. 2020Bayesian Deep Learning via Subnetwork Inferencecited by 85
4 more
  1. 2020The Case for Bayesian Deep Learningcited by 68
  2. 2022Do Bayesian Neural Networks Need To Be Fully Stochastic?cited by 63
  3. 2018Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Taskscited by 36
  4. 2018Never look back - A modified EnKF method and its application to the training of neural networks without back propagationcited by 8

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