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Large-scale supervised classification algorithms, especially those based on deep convolutional neural networks (DCNNs), require vast amounts of training data to achieve state-of-the-art performance. Decreasing this data requirement would significantly speed up the training process and possibly improve generalization. Motivated by this objective, we consider(More)
We introduce a new random graph model. In our model, n; n ≥ 2, vertices choose a subset of potential edges by considering the (estimated) benefits or utilities of the edges. More precisely, each vertex selects k, k ≥ 1, incident edges it wishes to set up, and an edge between two vertices is present in the graph if and only if both of the end(More)
We introduce a new random graph model. In our model, n, n ≥ 2, vertices choose a subset of potential edges by considering the (estimated) benefits or utilities of the edges. More precisely, each vertex selects k, k ≥ 1, incident edges it wishes to set up, and an undirected edge between two vertices is present in the graph if and only if both of the end(More)
We study the interaction between two different types of players in the presence of uncertainty in the payoffs. In particular, we examine a scenario where there is a reward for coordinating with the players of the other type and investigate how the players behave when they interact repeatedly under a simple selection model. The recurring interactions between(More)
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