Ensemble Kalman methods for training neural networks

Data assimilation for network weights

September 20, 2022 — November 29, 2024

Bayes
dynamical systems
likelihood free
linear algebra
machine learning
Monte Carlo
neural nets
nonparametric
particle
probability
sciml
signal processing
sparser than thou
statistics
statmech
stochastic processes
time series
uncertainty

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Figure 1

Training neural networks by ensemble Kalman updates instead of SGD. Arises naturally from the dynamical perspective on neural networks. TBD.

Claudia Schilling’s filter () is an elegant variant of the ensemble Kalman Filter which looks somehow more general than the original but also simpler and may be applicable. Haber, Lucka, and Ruthotto () use it to train neural nets (!) and show a rather beautiful connection to stochastic gradient descent in section 3.2.

1 References

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Chada, Iglesias, Roininen, et al. 2018. “Parameterizations for Ensemble Kalman Inversion.” Inverse Problems.
Chen, Chong, Dou, Chen, et al. 2022. “A Novel Neural Network Training Framework with Data Assimilation.” The Journal of Supercomputing.
Chen, Yuming, Sanz-Alonso, and Willett. 2022. “Autodifferentiable Ensemble Kalman Filters.” SIAM Journal on Mathematics of Data Science.
———. 2023. “Reduced-Order Autodifferentiable Ensemble Kalman Filters.” Inverse Problems.
Dunbar, Duncan, Stuart, et al. 2022. “Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods.” SIAM Journal on Applied Dynamical Systems.
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