An effort to formalize the process of software pipelining loops with conditions is presented in this paper. A formal framework for scheduling such loops, based on representing set...
We have designed and fabricated a VLSI synapse that can learn a conditional probability or correlation between spike-based inputs and feedback signals. The synapse is low power, c...
We propose a directed graphical representation of utility functions, called UCP-networks, that combines aspects of two existing preference models: generalized additive models and ...
Abstract − We address the estimation of quantiles from heavy-tailed distributions when functional covariate information is available and in the case where the order of the quanti...
Abstract. We consider the problem of learning a user's ordinal preferences on a multiattribute domain, assuming that her preferences are lexicographic. We introduce a general ...