How far can you trust your Deep Neural Networks? (feat. TensorFlow) (25분)

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https://goo.gl/iJQ7JG

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In this talk, I will mainly focus on inferring the uncertainty information when using a deep neural network in TensorFlow. In particular, regression tasks, which have been less focussed compared to classification problems, will be mainly considered. First, a mixture density network will be implemented with TensorFlow where its superiority will be shown compared to ordinary regression networks. Then, two different methods, epistemic uncertainty and the entropy of a Gaussian mixture model, will be presented to estimate the uncertainty information along with the prediction output.

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