We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, inst...
Strassen’s matrix multiplication (MM) has benefits with respect to any (highly tuned) implementations of MM because Strassen’s reduces the total number of operations. Strasse...
We develop a memory-efficient off-line algorithm for the enumeration of global states of a distributed computation. The algorithm allows the parameterization of its memory requir...
—In this paper, we present a UML metamodel-based approach for creating and executing workflow models. The modeling language is introduced through its abstract syntax, and an eval...