normflows: A PyTorch Package for Normalizing
Flows

Stimper, Vincent; Liu, David; Campbell, Andrew T.; Berenz, Vincent; Ryll, Lukas; Schoelkopf, Bernhard; Hernández-Lobato, José Miguel

doi:10.21105/joss.05361

Cited by 11 publications

(2 citation statements)

References 8 publications

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“…The normalizing flow model used for the present study implements the normalizing-flows python package [7]. The normalizing-flows package provides implementations of the affine coupling layers described in section 2.1.…”

Section: Jinst 19 C06020 3 Model Implementationmentioning

confidence: 99%

Normalizing flows for domain adaptation when identifying Λ hyperon events

Kelleher,

Vossen

2024

J. Inst.

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This study focuses on the application of a normalizing flow as a method of domain adaptation when classifying physics data. Normalizing flows offer a way to transform data points between two different distributions. The present study investigates a novel method of transforming latent representations of physics data to a normal distribution and then to a physics distribution again. The final distribution models a simulated distribution. After being transformed, the data can be classified by a neural network trained on labeled simulation data. The present study succeeds in training two normalizing flows that can transform between data (or simulation) and a Gaussian distribution.

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Section: Jinst 19 C06020 3 Model Implementationmentioning

confidence: 99%

Normalizing flows for domain adaptation when identifying Λ hyperon events

Kelleher,

Vossen

2024

J. Inst.

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“…It facilitates both (a) the differentiable implementation of simulation models by providing a common object-oriented framework for their implementation in PyTorch (Paszke et al, 2019), and (b) the use of a variety of gradient-assisted inference procedures for these simulation models, allowing researchers to easily exploit the differentiable nature of their simulator in parameter estimation tasks. The package consists of both Bayesian and non-Bayesian inference methods, and relies on well-supported software libraries (e.g., normflows, Stimper et al, 2023) to provide this broad functionality.…”

mentioning

confidence: 99%

BlackBIRDS: Black-Box Inference foR Differentiable Simulators

Quera-Bofarull,

Dyer,

Calinescu

et al. 2023

JOSS

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BlackBIRDS is a Python package consisting of generically applicable, black-box inference methods for differentiable simulation models. It facilitates both (a) the differentiable implementation of simulation models by providing a common object-oriented framework for their implementation in PyTorch (Paszke et al., 2019), and (b) the use of a variety of gradient-assisted inference procedures for these simulation models, allowing researchers to easily exploit the differentiable nature of their simulator in parameter estimation tasks. The package consists of both Bayesian and non-Bayesian inference methods, and relies on well-supported software libraries (e.g., normflows, Stimper et al., 2023) to provide this broad functionality.

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