Blonde Maria using ATM machine

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Maria is 25 years old, she has medium-length blonde hair, and is wearing navy blue shorts, a white t-shirt, and white sneakers. Maria is standing with her back to the camera, interacting with the touchscreen of an ATM. The image is a full-body portrait with photographic quality, white background, in 16k resolution

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graphical illustration of , to look for computationally tractable substitutes for its unattainable gold standard-the answer was hiding in plain sight. Dropout was an approximation of Bayesian uncertainty. They already had the measure they sought. Dropout was typically used only for training the model and was deliberately turned off when the models were used in practice, the idea was that you'd get maximally accurate (and totally consistent) predictions by training different subsets but then always actually using the entire model. Only what would happen if you left dropout on in a deployed system? By running a prediction multiple times, each with a different random portion of the network dropped out, you would get a bouquet of slightly different predictions. It was like getting an exponentially large ensemble for free out of a single model. The resulting uncertainty in the system's outputs doesn't just resemble the output of the ideal, but tragically uncomputable, Bayesian neural network. As it turns out, it is the output-at least, a close approximation, within rigorous theoretical bounds-of that ideal, uncomputable Bayesian neural network. The result has been a set of tools that put those once-impractical techniques within reach, making them available for practitioners to make use of in real applications. That's been a big, big change over the past few years, because now you can take these beautiful mathematics, yield some approximations, and then you can use these for interesting problems, --ar 16:9 --v 6.0 --no letter

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