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Columbia U’s Infinitely Deep Probabilistic Model Adapts Its Complexity to the Data at Hand

While today’s deep neural networks (DNNs) are driving AI’s deep-learning revolution, determining a DNN’s appropriate complexity remains challenging. If a DNN is too shallow, its predictive performance will suffer; if it is too deep, it will tend to overfit, and its complexity will result in prohibitively high compute costs.

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