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Found 7 papers, 0 papers with code
Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders
It approximates the posterior of the true model a priori; fixing this posterior approximation, we then maximize the lower bound relative to only the generative model.
An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks
Comparing Bayesian neural networks (BNNs) with different widths is challenging because, as the width increases, multiple model properties change simultaneously, and, inference in the finite-width case is intractable.
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks
Variational Auto-encoders (VAEs) are deep generative latent variable models that are widely used for a number of downstream tasks.
BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty
Traditional training of deep classifiers yields overconfident models that are not reliable under dataset shift.
Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using Multi-Headed Auxiliary Networks
Neural Linear Models (NLM) are deep Bayesian models that produce predictive uncertainties by learning features from the data and then performing Bayesian linear regression over these features.
Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders
Recent work shows that traditional training methods tend to yield solutions that violate modeling desiderata: (1) the learned generative model captures the observed data distribution but does so while ignoring the latent codes, resulting in codes that do not represent the data (e. g. van den Oord et al. (2017); Kim et al. (2018)); (2) the aggregate of the learned latent codes does not match the prior p(z).
Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables
Bayesian Neural Networks with Latent Variables (BNN+LVs) capture predictive uncertainty by explicitly modeling model uncertainty (via priors on network weights) and environmental stochasticity (via a latent input noise variable).
