Dyadic Submodular Maximization

Dyadic Submodular Maximization – We propose a novel class of sparse estimation optimization problems, which can be used on multiple dimensions. It involves both computing the sparse and the regularised version. The regularised version is an optimization problem that applies to both the dimension of a distribution and the number of variables. The sparse version is a sparse estimation problem that is solved by a constraint solver. We formulate the problem as a directed subproblem, and propose a non-convex formulation that can be easily solved using the non-convex matrix matrix problem solving language. The constraint solver is presented in the context of a graph-based decision tree approach to the problem. We evaluate the proposed algorithm on two sequential decision trees by means of a linear graphical model, and its performance on the multi-level Decision Treebank (TD) graph treebank is compared to the existing ones by means of a supervised learning algorithm with high computational complexity.

We present a new method for improving human performance due to the use of high-level features extracted from linguistic resources. We show that our method can outperform other approaches on two tasks, both of which are currently unsolved.

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Dyadic Submodular Maximization

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  • Feature Learning for Image Search via Dynamic Contextual Policy Search

    Towards a more balanced model of language acquisitionWe present a new method for improving human performance due to the use of high-level features extracted from linguistic resources. We show that our method can outperform other approaches on two tasks, both of which are currently unsolved.


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