Phylogenies without Branch Bounds: Contracting the Short, Pruning the Deep

Phylogenies without Branch Bounds: Contracting the Short, Pruning the Deep

We introduce a new phylogenetic reconstruction algorithm which, unlike most previous rigorous inference techniques, does not rely on assumptions regarding the branch lengths or the depth of the tree. The algorithm returns a forest which is guaranteed to contain all edges that are: 1) sufficiently long and 2) sufficiently close to the leaves. How much of the true tree is recovered depends on the...

On Deep e-Contracting

The use of electronic contracts is emerging as a way to improve the efficiency of contracting processes. Electronic contracts are, however, often used as a direct replacement for traditional paper contracts – which we call shallow e-contracting. Consequently, business processes in general and contracting processes in particular do not change much through the use of electronic contracts. New bus...

On the Boosting Pruning Problem (short Submission)

Boosting is a powerful method for improving the predictive accuracy of classiiers. The AdaBoost algorithm of Freund and Schapire has been successfully applied to many domains 2, 10, 12] and the combination of AdaBoost with the C4.5 decision tree algorithm has been called the best oo-the-shelf learning algorithm in practice. Unfortunately, in some applications, the number of decision trees requi...

BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning

Understanding the global optimality in deep learning (DL) has been attracting more and more attention recently. Conventional DL solvers, however, have not been developed intentionally to seek for such global optimality. In this paper we propose a novel approximation algorithm, BPGrad, towards optimizing deep models globally via branch and pruning. Our BPGrad algorithm is based on the assumption...

Pruning by Isomorphism in Branch-and-Cut