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Sample Efficient Uncertainty Estimation for Deep Learning Safety
Deep Neural Networks (DNNs) are known to make highly overconfident predi...
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Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection
Deep neural networks (DNNs) have been widely applied for detecting COVID...
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Interpreting Uncertainty in Model Predictions For COVID-19 Diagnosis
COVID-19, due to its accelerated spread has brought in the need to use a...
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Uncertainty Estimation with Infinitesimal Jackknife, Its Distribution and Mean-Field Approximation
Uncertainty quantification is an important research area in machine lear...
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Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
Uncertainty estimation and ensembling methods go hand-in-hand. Uncertain...
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Enhancing the Robustness of Prior Network in Out-of-Distribution Detection
With the recent surge of interests in deep neural networks, more real-wo...
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One Versus all for deep Neural Network Incertitude (OVNNI) quantification
Deep neural networks (DNNs) are powerful learning models yet their resul...
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Can Your AI Differentiate Cats from Covid-19? Sample Efficient Uncertainty Estimation for Deep Learning Safety
Deep Neural Networks (DNNs) are known to make highly overconfident predictions on Out-of- Distribution data. Recent research has shown that uncertainty-aware models, such as, Bayesian Neural Network (BNNs) and Deep Ensembles, are less susceptible to this issue. However research in this area has been largely confined to the big data setting. In this work, we show that even state-of-the-art BNNs and Ensemble models tend to make overconfident predictions when the amount of training data is insufficient. This is especially concerning for emerging applications in physical sciences and healthcare where overconfident and inaccurate predictions can lead to disastrous consequences. To address the issue of accurate uncertainty (or confidence) estimation in the small-data regime, we propose a probabilistic generalization of the popular sample-efficient non-parametric kNN approach. We demonstrate the usefulness of the proposed approach on a real-world application of COVID-19 diagnosis from chest X-Rays by (a) highlighting surprising failures of existing techniques, and (b) achieving superior uncertainty quantification as compared to state-of-the-art.
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