
Revisiting the Calibration of Modern Neural Networks
Accurate estimation of predictive uncertainty (model calibration) is ess...
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Uncertainty Baselines: Benchmarks for Uncertainty Robustness in Deep Learning
Highquality estimates of uncertainty and robustness are crucial for num...
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RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
The development of recommender systems that optimize multiturn interact...
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Combining Ensembles and Data Augmentation can Harm your Calibration
Ensemble methods which average over multiple neural network predictions ...
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Training independent subnetworks for robust prediction
Recent approaches to efficiently ensemble neural networks have shown tha...
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Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Ensembles over neural network weights trained from different random init...
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Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Bayesian neural networks (BNN) and deep ensembles are principled approac...
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Efficient and Scalable Bayesian Neural Nets with Rank1 Factors
Bayesian neural networks (BNNs) demonstrate promising success in improvi...
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On the Discrepancy between Density Estimation and Sequence Generation
Many sequencetosequence generation tasks, including machine translatio...
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BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning
Ensembles, where multiple neural networks are trained individually and t...
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Analyzing the Role of Model Uncertainty for Electronic Health Records
In medicine, both ethical and monetary costs of incorrect predictions ca...
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Discrete Flows: Invertible Generative Models of Discrete Data
While normalizing flows have led to significant advances in modeling hig...
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Measuring Calibration in Deep Learning
The reliability of a machine learning model's confidence in its predicti...
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NeuTralizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
Hamiltonian Monte Carlo is a powerful algorithm for sampling from diffic...
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Bayesian Layers: A Module for Neural Network Uncertainty
We describe Bayesian Layers, a module designed for fast experimentation ...
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Autoconj: Recognizing and Exploiting Conjugacy Without a DomainSpecific Language
Deriving conditional and marginal distributions using conjugacy relation...
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Simple, Distributed, and Accelerated Probabilistic Programming
We describe a simple, lowlevel approach for embedding probabilistic pro...
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MeshTensorFlow: Deep Learning for Supercomputers
Batchsplitting (dataparallelism) is the dominant distributed Deep Neur...
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Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
Obtaining reliable uncertainty estimates of neural network predictions i...
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Flipout: Efficient PseudoIndependent Weight Perturbations on MiniBatches
Stochastic neural net weights are used in a variety of contexts, includi...
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TensorFlow Distributions
The TensorFlow Distributions library implements a vision of probability ...
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Implicit Causal Models for Genomewide Association Studies
Progress in probabilistic generative models has accelerated, developing ...
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Hierarchical Implicit Models and LikelihoodFree Variational Inference
Implicit probabilistic models are a flexible class of models defined by ...
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Deep Probabilistic Programming
We propose Edward, a Turingcomplete probabilistic programming language....
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Variational Inference via χUpper Bound Minimization
Variational inference (VI) is widely used as an efficient alternative to...
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Edward: A library for probabilistic modeling, inference, and criticism
Probabilistic modeling is a powerful approach for analyzing empirical in...
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Operator Variational Inference
Variational inference is an umbrella term for algorithms which cast Baye...
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Automatic Differentiation Variational Inference
Probabilistic modeling is iterative. A scientist posits a simple model, ...
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The Variational Gaussian Process
Variational inference is a powerful tool for approximate inference, and ...
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Hierarchical Variational Models
Black box variational inference allows researchers to easily prototype a...
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Stochastic gradient descent methods for estimation with large data sets
We develop methods for parameter estimation in settings with largescale...
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Copula variational inference
We develop a general variational inference method that preserves depende...
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Towards stability and optimality in stochastic gradient descent
Iterative procedures for parameter estimation based on stochastic gradie...
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Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
A common approach for Bayesian computation with big data is to partition...
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Convex Techniques for Model Selection
We develop a robust convex algorithm to select the regularization parame...
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