In this paper we present a new method, time-striding hidden Markov model (TSHMM), to learn from long-term motion for atomic behaviors and the statistical dependencies among them. T...
Abstract. In this paper we unify divergence minimization and statistical inference by means of convex duality. In the process of doing so, we prove that the dual of approximate max...
Abstract. Computation time is an important performance characteristic of computer vision algorithms. This paper shows how existing (slow) binary-valued decision algorithms can be a...
— Area Under the ROC Curve (AUC) is often used to evaluate ranking performance in binary classification problems. Several researchers have approached AUC optimization by approxi...
We study the weight dynamics of the floating-gate pFET synapse and the effects of the pFET's gate and drain voltages on these dynamics. We show that we can derive a weight upd...