As training data become larger, distributed training has been broadly adopted in machine learning tasks.

Frequently,we are also interested in the scores associated with the objects to deduce the intensity of the resulting preference.

The assistant is helpful, creative,clever,andveryfriendly.

Somewhat surprisingly, learning in a Markov Decision Process is most often considered under the performance criteria ofconsistency or regret minimization(see e.g.

Figure 2: RN "100) when structure (see(A)stationary (B)altered T{3and2T{3as

This formalizes a balance between learning low-dimensional representations and minimizing complexity/irregularity in the feature maps, allowing the network to learn the'right' inner dimension.

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