571 citations · 1.7k across the 29 of their papers we have counts for
16 papers · 1 filter
Morse Neural Networks for Uncertainty Quantification
Benoit Dherin, Huiyi Hu, Jie Ren +2
We introduce a new deep generative model useful for uncertainty quantification: the Morse neural network, which generalizes the unnormalized Gaussian densities to have modes of hig…
Bayesian Deep Ensembles via the Neural Tangent Kernel
Bobby He, Balaji Lakshminarayanan, Yee Whye Teh
We explore the link between deep ensembles and Gaussian processes (GPs) through the lens of the Neural Tangent Kernel (NTK): a recent development in understanding the training dyna…
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Dan Hendrycks, Norman Mu, Ekin D. Cubuk +3
Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated i…
Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende +2
Normalizing flows provide a general mechanism for defining expressive probability distributions, only requiring the specification of a (usually simple) base distribution and a seri…
Deep Ensembles: A Loss Landscape Perspective
Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan
Deep ensembles have been empirically shown to be a promising approach for improving accuracy, uncertainty and out-of-distribution robustness of deep learning models. While deep ens…
Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh +1
Recent work has shown that deep generative models can assign higher likelihood to out-of-distribution data sets than to their training data (Nalisnick et al., 2019; Choi et al., 20…