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- Maya Kabkab, Azadeh Alavi, Rama Chellappa
- ArXiv
- 2016

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)

- Richard J. La, Maya Kabkab
- 2013 51st Annual Allerton Conference on…
- 2013

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)

- Maya Kabkab, Richard J. La
- 2012 46th Annual Conference on Information…
- 2012

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)

- Maya Kabkab, Emily M. Hand, Rama Chellappa
- ICPR
- 2016

- Richard J. La, Maya Kabkab
- Internet Mathematics
- 2015

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)

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