5.7k citations · 8.4k across the 42 of their papers we have counts for
14 papers · 1 filter
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit
Ben Adlam, Jaehoon Lee, Lechao Xiao +2
Modern deep learning models have achieved great success in predictive accuracy for many data modalities. However, their application to many real-world tasks is restricted by poor u…
Combining Ensembles and Data Augmentation can Harm your Calibration
Yeming Wen, Ghassen Jerfel, Rafael Muller +4
Ensemble methods which average over multiple neural network predictions are a simple approach to improve a model's calibration and robustness. Similarly, data augmentation techniqu…
Training independent subnetworks for robust prediction
Marton Havasi, Rodolphe Jenatton, Stanislav Fort +5
Recent approaches to efficiently ensemble neural networks have shown that strong robustness and uncertainty performance can be achieved with a negligible gain in parameters over th…
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures
Benjamin Kompa, Jasper Snoek, Andrew Beam
Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes, real-world settings. Currently, the qualit…
A Spectral Energy Distance for Parallel Speech Synthesis
Alexey A. Gritsenko, Tim Salimans, Rianne van den Berg +2
Speech synthesis is an important practical generative modeling problem that has seen great progress over the last few years, with likelihood-based autoregressive neural models now…
Cold Posteriors and Aleatoric Uncertainty
Ben Adlam, Jasper Snoek, Samuel L. Smith
Recent work has observed that one can outperform exact inference in Bayesian neural networks by tuning the "temperature" of the posterior on a validation set (the "cold posterior"…
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks
Shreyas Padhy, Zachary Nado, Jie Ren +3
Accurate estimation of predictive uncertainty in modern neural networks is critical to achieve well calibrated predictions and detect out-of-distribution (OOD) inputs. The most pro…
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Florian Wenzel, Jasper Snoek, Dustin Tran +1
Ensembles over neural network weights trained from different random initialization, known as deep ensembles, achieve state-of-the-art accuracy and calibration. The recently introdu…
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift
Zachary Nado, Shreyas Padhy, D. Sculley +3
Covariate shift has been shown to sharply degrade both predictive accuracy and the calibration of uncertainty estimates for deep learning models. This is worrying, because covariat…
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen +5
Bayesian neural networks (BNNs) demonstrate promising success in improving the robustness and uncertainty quantification of modern deep learning. However, they generally struggle w…
Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling
Setareh Ariafar, Zelda Mariet, Ehsan Elhamifar +3
Many contemporary machine learning models require extensive tuning of hyperparameters to perform well. A variety of methods, such as Bayesian optimization, have been developed to a…
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling +7
Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights. Recent work developing this class of methods…
How Good is the Bayes Posterior in Deep Neural Networks Really?
Florian Wenzel, Kevin Roth, Bastiaan S. Veeling +7
During the past five years the Bayesian deep learning community has developed increasingly accurate and efficient approximate inference procedures that allow for Bayesian inference…
Hydra: Preserving Ensemble Diversity for Model Distillation
Linh Tran, Bastiaan S. Veeling, Kevin Roth +7
Ensembles of models have been empirically shown to improve predictive performance and to yield robust measures of uncertainty. However, they are expensive in computation and memory…