submitted by gwern to MachineLearning [link] [comments]
Aleatoric uncertainty. Aleatoric uncertainty captures our uncertainty with respect to information which our data cannot explain. For example, aleatoric uncertainty in images can be attributed to occlusions (because cameras can’t see through objects) or lack of visual features or over-exposed regions of an image, etc. It can be explained away with the ability to observe all explanatory variables with increasing precision. Aleatoric uncertainty is very important to model for: For aleatoric uncertainty, we mean the uncertainty inherent inside the randomness of data, and it can not decrease giving more training data. Epistemic uncertainty, on the other hand, comes from our lack of knowledge. In the context of modeling, it comes from the defects in model structure or weights. Aleatoric uncertainty is the uncertainty arising from the natural stochasticity of observations. Aleatoric uncertainty cannot be reduced even when more data is provided. When it comes to measurement errors, we call it homoscedastic uncertainty because it is constant for all samples. Input data-dependent uncertainty is known as heteroscedastic uncertainty. Aleatoric uncertainty accounts for noise inherent in the observations due to class overlap, label noise, homoscedastic and heteroscedastic noise, which cannot be reduced even if more data were to be collected. In X-ray imaging, this can be caused by sensor noise due to random distribution of photons during scan acquisition. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for uncertainty in the model -- uncertainty which can be explained away given enough data. Traditionally it has been difficult to model epistemic uncertainty in computer vision, but with new Bayesian deep learning tools this is now possible. We study the benefits of modeling Introduction This post is aimed at explaining the concept of uncertainty in deep learning. More often than not, when people speak of uncertainty or probability in deep learning, many different concepts of uncertainty are interchanged with one another, confounding the subject in hand altogether. To see this, consider such questions. - Is my network's classification… Since both types of uncertainty are not constant throughout all predictions, we need a way of assigning a specific uncertainty to each prediction. That’s where the new TensorFlow Probability package steps in to save the day. It provides a framework that combines probabilistic modeling with the power of our beloved deep learning models. [5], uncertainty quantification is a problem of paramount importance when deploying machine learn-ing models in sensitive domains such as information security [72], engineering [82], transportation [87], and medicine [5], to name a few. Despite its importance, uncertainty quantification is a largely unsolved problem. Prior literature on uncertainty estimation for deep neural networks is dominated by Bayesian methods [37, 6, 25, 44, 45, aleatoric and epistemic uncertainty (Hora, 1996). Roughly speaking, aleatoric (aka statistical) uncertainty refers to the notion of randomness, that is, the variability in the outcome of an experiment which is due to inherently random effects. The prototypical example of aleatoric uncertainty is coin flipping: The data-generating process in this type of experiment has a stochastic component that cannot be reduced by whatsoever additional source of information (except Laplace’s demon 6.7 Epistemic,Aleatoric,andPredictiveuncertainties . . . . . . . . . . . . .127 7 FutureResearch133 References137 AppendixA KLcondition149 AppendixB Figures153 AppendixC SpikeandslabpriorKL159. Nomenclature RomanSymbols A matrix a vector a scalar W Weightmatrix D Dataset X Datasetinputs(matrixwithNrows,oneforeachdatapoint) Y Datasetoutputs(matrixwithNrows,oneforeachdatapoint) x n
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PyData Warsaw 2018We will show how to assess the uncertainty of deep neural networks. We will cover Bayesian Deep Learning and other out-of-distribution dete... CUAHSI's 2019 Spring Cyberseminar Series on Recent advances in big data machine learning in HydrologyDate: April 19, 2019Topic: Long-term projections of soil... Aleatoric Music: Live Looping ... Modern Deep Learning through Bayesian Eyes - Duration: 1:00:53. ... PyData Tel Aviv Meetup: Uncertainty in Deep Learning - Inbar Naor - Duration: ... Based on those translated images, the trained uncertainty-aware imitation learning policy would output both the predicted action and the data uncertainty motivated by the aleatoric loss function.
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