Artificial Intelligence Stack Exchange
Artificial Intelligence Stack Exchange

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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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 set a threshold of 90% certainty below which my…
Alexander Soare
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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?
rudresh dwivedi
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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?" is located before box 6 and box 9 which are the processes…
Lerner Zhang
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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 problems, we acknowledge that we are not skilled…
A Cat Named Tiger
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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 it is correct given the distribution of data it…
user8714896
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How do language models know what they don't know - and report it?

Again and again I ask myself what goes on in a pre-trained transformer-based language model (like ChatGPT9) when it comes to "know" that it cannot give an appropriate answer and either states it ("I have not enough information to answer this…
Hans-Peter Stricker
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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 Bayesian Deep Learning for Computer Vision, and the…
user8714896
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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, for example, average these predictions. Let's…
lebebop
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What are the leading methods to estimate Epistemic Uncertainty in Large Language Models?

Epistemic uncertainty is uncertainty that arises from a lack of knowledge, for instance in machine learning epistemic uncertainty can be caused by a lack of training data. Estimating epistemic uncertainty is important for useful AI systems, since it…
Rexcirus
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How Mutual Information is related to uncertainty

I'm studying the chapter of Information theory from Haykin's deep learning book. It says Mutual Information between two continuous random variables $X,Y$ is defined in terms of the differential entropies $h(\cdot)$ as $I(X;Y)=h(X)-h(X|Y) =…
piero
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What does AUSE metric mean in uncertainty estimation

I am reading the paper "Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision", I do not understand the definition of AUSE metric in this sentence "but only in terms of the AUSE metric which is a relative measure of the…
TimothyShi
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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 test with train being around 20 points. The test serves as the unlabelled pool (although I have the…
Antonios Sarikas
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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: >>> predictions = np.random.rand(3, 1, 5) [[[0.22668968…
HenDoNR
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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 (epistemic). The model outputs bounding boxes with…
Baka
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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 using the tensorflow.keras.applications.VGG16 class as base model with…
Matthias
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