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What uncertainties do we need in bayesian deep learning for computer vision?
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How is uncertainty depicted in a dropout mask?
Uncertainty depictions in my previous blog posts drew new dropout masks for each test point—which is equivalent to drawing a new prediction from the predictive distribution for each test point − 2 ≤ x ≤ 2 .
What causes uncertainty in dropout Bayesian neural networks?
There are two factors at play when visualising uncertainty in dropout Bayesian neural networks: the dropout masks and the dropout probability of the first layer.
How to obtain uncertainty in deep learning ( PhD thesis )?
So I finally submitted my PhD thesis (given below ). In it I organised the already published results on how to obtain uncertainty in deep learning, and collected lots of bits and pieces of new research I had lying around (which I hadn't had the time to publish yet).
How are geometry and uncertainty used in deep learning?
This thesis explores these ideas using concepts from geometry and uncertainty. Specifically, we show how to improve end-to-end deep learning models by leveraging the underlying geometry of the problem. We explicitly model concepts such as epipolar geometry to learn with unsupervised learning, which improves performance.
Last Update: Oct 2021
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This dissertation studies the effects of dubiety shocks in nascent economies in A context where these economies increased their participation in foreign financial markets.
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