The definition of Sinkhorn divergences usually starts from that oftheground cost onobservations.

Deep network models are often purely inductive during both training and inference on unseen data. When these models are used for prediction, but they may fail to capture important semantic information and implicit dependencies within datasets. Recent advancements have shown that combining multiple modalities in large-scale vision and language settings can improve understanding and generalization performance.

We call this family of functions intra order-preserving functions. We propose a new neural network architecture that represents a class of intra order-preserving functions by combining common neural network components.

The premise of semi-supervised learning (SSL) is that combining labeled and unlabeled data yields significantly more accurate models.

Theoptimalbaseline, however, israrelyusedinpractice (Sutton & Barto (2018); foran exception, see (Peters & Schaal, 2008)). Equation (1) thentakesthefollowingform: r E R(x)= E (R(x) B)r log (x).

Twoofthemcanbecharacterizedas "Reward Maximization" (RM): Standard Policy Gradients (PG) and KL-control.

The problem of learning the parameters of a neural network is two-fold. First, we want that their training on a set of data via minimization of a suitable loss function succeed in finding a set of parameters for which the value of the loss is close to its global minimum.