Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...
Modern classification applications necessitate supplementing the few available labeled examples with unlabeled examples to improve classification performance. We present a new tra...
When accounting for structural fluctuations or measurement errors, a single rigid structure may not be sufficient to represent a protein. One approach to solve this problem is to r...
Background: The importance of stochasticity in cellular processes having low number of molecules has resulted in the development of stochastic models such as chemical master equat...
Principal components and canonical correlations are at the root of many exploratory data mining techniques and provide standard pre-processing tools in machine learning. Lately, p...