We focus on the problem of efficient learning of dependency trees. Once grown, they can be used as a special case of a Bayesian network, for PDF approximation, and for many other u...
In this paper, we give a simple scheme for identifying approximate frequent items over a sliding window of size n. Our scheme is deterministic and does not make any assumption on ...
Abstract. Streaming environments typically dictate incomplete or approximate algorithm execution, in order to cope with sudden surges in the data rate. Such limitations are even mo...
We present the first efficient approach to global routing that takes spacing-dependent costs into account and provably finds a near-optimum solution including these costs. We sh...
— We present a waveform based variational static timing analysis methodology. It is a timing paradigm that lies midway between convention static delay approximations and full dyn...