Abstract. A crucial problem in machine learning is to choose an appropriate representation of data, in a way that emphasizes the relations we are interested in. In many cases this ...
Several features such as reconfiguration, voltage and frequency scaling, low-power operating states, duty-cycling, etc. are exploited for latency and energy efficient application ...
Abstract-- Increasing delay and power variation are significant challenges to the designers as technology scales to the deep sub-micron (DSM) regime. Traditional module selection t...
We consider the problem of learning incoherent sparse and lowrank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the spa...
rocesses involve modeling – simplifying or abstracting some aspects of the problem domain in order to plan and evaluate design decisions. The use of representations to reason abo...