Add to ORKGWhereas the ability of deep networks to produce useful predictions on many kinds of data has been amply demonstrated, estimating the reliability of these predictions remains challenging. Sampling approaches such as MC-Dropout and Deep Ensembles have emerged as the most popular ones for this purpose. Unfortunately, they require many forward passes at inference time, which slows them down. Sampling-free approaches can be faster but suffer from other drawbacks, such as lower reliability of uncertainty estimates, difficulty of use, and limited applicability to different types of tasks and data. In this work, we introduce a sampling-free approach that is generic and easy to deploy, while producing reliable uncertainty estimates on par with sta...
Deep Ensembles, as a type of Bayesian Neural Networks, can be used to estimate uncertainty on the pr...
We are interested in estimating the uncertainties of deep neural networks, which play an important r...
As neural networks become more popular, the need for accompanying uncertainty estimates increases. T...
Deep neural networks have amply demonstrated their prowess but estimating the reliability of their p...
Uncertainty estimation (UE) techniques -- such as the Gaussian process (GP), Bayesian neural network...
Uncertainty estimation for machine learning models is of high importance in many scenarios such as c...
Estimating the uncertainty in deep neural network predictions is crucial for many real-world applica...
Uncertainty quantification (UQ) is important for reliability assessment and enhancement of machine l...
Neural networks predictions are unreliable when the input sample is out of the training distribution...
With the rise of the popularity and usage of neural networks, trustworthy uncertainty estimation is ...
The ability to estimate epistemic uncertainty is often crucial when deploying machine learning in th...
Deep neural networks (NNs) are known for their high-prediction performances. However, NNs are prone ...
Considering uncertainty estimation of modern neural networks (NNs) is one of the most important ste...
Accurate uncertainty quantification is necessary to enhance the reliability of deep learning models ...
Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning mo...
Deep Ensembles, as a type of Bayesian Neural Networks, can be used to estimate uncertainty on the pr...
We are interested in estimating the uncertainties of deep neural networks, which play an important r...
As neural networks become more popular, the need for accompanying uncertainty estimates increases. T...
Deep neural networks have amply demonstrated their prowess but estimating the reliability of their p...
Uncertainty estimation (UE) techniques -- such as the Gaussian process (GP), Bayesian neural network...
Uncertainty estimation for machine learning models is of high importance in many scenarios such as c...
Estimating the uncertainty in deep neural network predictions is crucial for many real-world applica...
Uncertainty quantification (UQ) is important for reliability assessment and enhancement of machine l...
Neural networks predictions are unreliable when the input sample is out of the training distribution...
With the rise of the popularity and usage of neural networks, trustworthy uncertainty estimation is ...
The ability to estimate epistemic uncertainty is often crucial when deploying machine learning in th...
Deep neural networks (NNs) are known for their high-prediction performances. However, NNs are prone ...
Considering uncertainty estimation of modern neural networks (NNs) is one of the most important ste...
Accurate uncertainty quantification is necessary to enhance the reliability of deep learning models ...
Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning mo...
Deep Ensembles, as a type of Bayesian Neural Networks, can be used to estimate uncertainty on the pr...
We are interested in estimating the uncertainties of deep neural networks, which play an important r...
As neural networks become more popular, the need for accompanying uncertainty estimates increases. T...