9 citations · 26 across the 13 of their papers we have counts for
4 papers · 1 filter
BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty
Théo Guénais, Dimitris Vamvourellis, Yaniv Yacoby +2
Traditional training of deep classifiers yields overconfident models that are not reliable under dataset shift. We propose a Bayesian framework to obtain reliable uncertainty estim…
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks
Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez
Variational Auto-encoders (VAEs) are deep generative latent variable models that are widely used for a number of downstream tasks. While it has been demonstrated that VAE training…
Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using Multi-Headed Auxiliary Networks
Sujay Thakur, Cooper Lorsung, Yaniv Yacoby +2
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 the…
Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders
Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez
Variational Auto-encoders (VAEs) are deep generative latent variable models consisting of two components: a generative model that captures a data distribution p(x) by transforming…