Affine Independent Variational Inference

By Edward Challis, David Barber. 2012

20 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods

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In Probabilistic & Bayesian Methods

1. Qiang Liu
2. David M. Blei
3. Dustin Tran
4. Rajesh Ranganath
5. Michalis K. Titsias
6. Dilin Wang
7. Jun Zhu
8. Max Welling

Citations in the corpus

• Variational Inference with Normalizing Flows (2015, methodology): “This approach has also been referred to or as stochastic gradient variational Bayes (SGVB) (Kingma & Welling, 2014) or as affine variational inference (Challis & Barber, 2012).”
• Automatic Differentiation Variational Inference (2016, background): “Challis and Barber (2012) study Fourier transform techniques for location-scale variational approximations beyond the Gaussian.”
• Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm (2016, background): “Several other works also leverage variable transforms in variational inference, but with more limited forms; examples include affine transforms [19, 20], and recently the copula models that correspond to element-wise transforms over the individual variables [21, 22].”
• Geometrical Insights for Implicit Generative Modeling (2017, background): “The reparametrization trick [13, 42], Variational Auto-Encoders (VAEs) [27], and Generative Adversarial Networks (GANs) [20] are recent instances of this approach.”