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stat.ML2017
A Separation Principle for Control in the Age of Deep Learning
Alessandro Achille, Stefano Soatto
We review the problem of defining and inferring a "state" for a control system based on complex, high-dimensional, highly uncertain measurement streams such as videos. Such a state…
stat.ML2016★ 15 cited
Information Dropout: Learning Optimal Representations Through Noisy Computation
Alessandro Achille, Stefano Soatto
The cross-entropy loss commonly used in deep learning is closely related to the defining properties of optimal representations, but does not enforce some of the key properties. We…