The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects. Learning to rank is useful for document retrieval, collaborative fil...
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Han...
We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density func...
Many machine learning algorithms can be formulated as a generalized eigenvalue problem. One major limitation of such formulation is that the generalized eigenvalue problem is comp...
Packet-switched networks-on-chip (NoC) have been proposed as an efficient communication infrastructure for multi-core architectures. Adding virtual channels to a NoC helps to avoi...
We introduce a convex relaxation framework to optimally
minimize continuous surface ratios. The key idea is to minimize
the continuous surface ratio by solving a sequence
of con...
Kalin Kolev (University of Bonn), Daniel Cremers (...