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Conditional Expectation and Machine Learning

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The problem addressed by machine learning can also be formulated as one of computing conditional expectation, an approach little explored because of the success of machine learning. The focus of this presentation is to view conditional expectation, not as an alternative, but as a means of performance enhancement for machine learning. In particular, we show that conventional machine learning is itself a vehicle for computing conditional expectation, both post training and during training. A neural network architecture that combine conventional machine learning and computing conditional expectation will be presented.

This talk is part of the Machine Learning Reading Group @ CUED series.

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CRUK Cambridge Research Institute
Li Ka Shing Centre Location: Zoom. See CSAR website for joining information. Bateman Auditorium, Gonville & Caius College

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