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11/23/2022
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks
Bayesian deep learning seeks to equip deep neural networks with the abil...
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05/01/2022
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Accurate uncertainty quantification is a major challenge in deep learnin...
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10/07/2021
Sparse MoEs meet Efficient Ensembles
Machine learning models based on the aggregated outputs of submodels, ei...
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06/30/2021
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence
Variational Inference (VI) is a popular alternative to asymptotically ex...
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06/07/2021
Uncertainty Baselines: Benchmarks for Uncertainty Robustness in Deep Learning
High-quality estimates of uncertainty and robustness are crucial for num...
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11/06/2020
Underspecification Presents Challenges for Credibility in Modern Machine Learning
ML models often exhibit unexpectedly poor behavior when they are deploye...
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10/19/2020
Combining Ensembles and Data Augmentation can Harm your Calibration
Ensemble methods which average over multiple neural network predictions ...
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05/14/2020
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Bayesian neural networks (BNNs) demonstrate promising success in improvi...
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06/10/2019
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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04/30/2019
AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles
AdaNet is a lightweight TensorFlow-based (Abadi et al., 2015) framework ...
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04/02/2019
Measuring Calibration in Deep Learning
The reliability of a machine learning model's confidence in its predicti...
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12/14/2018
Online gradient-based mixtures for transfer modulation in meta-learning
Learning-to-learn or meta-learning leverages data-driven inductive bias ...
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08/17/2016