Questions tagged [uncertainty-quantification]

For questions about uncertainty quantification (aka uncertainty estimation) in the context of artificial intelligence, in particular, in the context of Bayesian machine learning.

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Active Learning regression with Random Forest

I have a dataset of about 8k points and I am trying to employ active learning with the random forest regressor. I have split the dataset to train and ...
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How to calculate uncertainty in Deep Ensembles for Reinforcement Learning?

Lets take the following example: I must predict the return (Q-values) of x state-action pairs using an ensemble of m models. Using NumPy I could have the following for x = 5 and m = 3: ...
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Why would the "improvement" be the result of random initialization, and so why should we use multiple runs?

I got this feedback for my thesis paper. The improvement shown in the results section could be the result of random initialization. There should be multiple runs with means and standard deviations. ...
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Does MobileNet SSD v2 only capture aleatoric uncertainty (and so not the epistemic one)?

Regarding the MobileNet SSD v2 model, I was wondering to what extend it captures uncertainty of the predictions. There are 2 types of uncertainty, data uncertainty (aleatoric) and model uncertainty (...
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What are the standard ways to measure the quality of a set of numerical predictions that include uncertainties?

I have a radial basis function that supplies uncertainties (standard deviations) with its predictions, which are numerical values. This function is computed for a particular point by computing its ...
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Do we need as much information to know if we can can answer a question as we need to actually answer the question?

I am reading The Book of Why: The New Science of Cause and Effect by Judea Pearl, and in page 12 I see the following diagram. The box on the right side of box 5 "Can the query be answered?" ...
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3 votes
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Why does this formula $\sigma^2 + \frac{1}{T}\sum_{t=1}^Tf^{\hat{W_t}}(x)^Tf^{\hat{W_t}}(x_t)-E(y)^TE(y)$ approximate the variance?

How does: $$\text{Var}(y) \approx \sigma^2 + \frac{1}{T}\sum_{t=1}^Tf^{\hat{W_t}}(x)^Tf^{\hat{W_t}}(x_t)-E(y)^TE(y)$$ approximate variance? I'm currently reading What Uncertainties Do We Need in ...
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3 votes
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How can I use Monte Carlo Dropout in a pre-trained CNN model?

In Monte Carlo Dropout (MCD), I know that I should enable dropout during training and testing, then get multiple predictions for the same input $x$ by performing multiple forward passes with $x$, then,...
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Why is my Keras prediction always close to 100% for one image class?

I am using Keras (on top of TF 2.3) to train an image classifier. In some cases I have more than two classes, but often there are just two classes (either "good" or "bad"). I am ...
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Is there any research on models that provide uncertainty estimation?

Is there any research on machine learning models that provide uncertainty estimation? If I train a denoising autoencoder on words and put through a noised word, I'd like it to return a certainty that ...
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35 votes
6 answers
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Why do CNN's sometimes make highly confident mistakes, and how can one combat this problem?

I trained a simple CNN on the MNIST database of handwritten digits to 99% accuracy. I'm feeding in a bunch of handwritten digits, and non-digits from a document. I want the CNN to report errors, so I ...
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7 votes
1 answer
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How does the Dempster-Shafer theory differ from Bayesian reasoning?

How does the Dempster-Shafer theory differ from Bayesian reasoning? How do these two methods handle uncertainty and compute posterior distributions?
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5 votes
5 answers
391 views

How would AI be able to self-examine?

As I see some cases of machine-learning based artificial intelligence, I often see they make critical mistakes when they face inexperienced situations. In our case, when we encounter totally new ...
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