Failures of Gradient-Based Deep Learning
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چکیده
We make the (non-realistic) assumption that every instance of media reference is strongly and positively correlated to a single stock y 2 [k], and it has no correlation with future performance of other stocks. This obviously makes our problem rather toyish; the stock exchange and media worlds have highly complicated correlations. However, it indeed arises from, and is motivated by, practical problems. To examine the problem in a simple and theoretically clean manner, we design a synthetic experiment defined by the following optimization problem: Let X ⇥ Z ⇢ R ⇥ {±1} be the sample space, and let y : X ! [k] be some labelling function. We would like to learn a mapping N
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