Uncertainty Quantification
753 papers with code • 0 benchmarks • 4 datasets
Benchmarks
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Libraries
Use these libraries to find Uncertainty Quantification models and implementations
MUAD
LUMA
JAMBO
Uncertainty Quantification for Underwater Object Segmentation
Most implemented papers
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost.
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
ENSTA-U2IS-AI/torch-uncertainty
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NeurIPS 2017
Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks.
Evidential Deep Learning to Quantify Classification Uncertainty
labmlai/annotated_deep_learning_paper_implementations
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NeurIPS 2018
Deterministic neural nets have been shown to learn effective predictors on a wide range of machine learning problems.
A Simple Baseline for Bayesian Uncertainty in Deep Learning
wjmaddox/swa_gaussian
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NeurIPS 2019
We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning.
Deep Evidential Regression
aamini/evidential-deep-learning
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NeurIPS 2020
We demonstrate learning well-calibrated measures of uncertainty on various benchmarks, scaling to complex computer vision tasks, as well as robustness to adversarial and OOD test samples.
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
Many important tasks in chemistry revolve around molecules during reactions.
Laplace Redux -- Effortless Bayesian Deep Learning
AlexImmer/Laplace
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NeurIPS 2021
Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty quantification and model selection.
BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning
We also apply BatchEnsemble to lifelong learning, where on Split-CIFAR-100, BatchEnsemble yields comparable performance to progressive neural networks while having a much lower computational and memory costs.
Uncertainty Sets for Image Classifiers using Conformal Prediction
aangelopoulos/conformal-classification
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ICLR 2021
Convolutional image classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, hindering their deployment in consequential settings.
Unsupervised Quality Estimation for Neural Machine Translation
pytorch/fairseq
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Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time.
