To learn a new visual category from few examples, prior knowledge from unlabeled data as well as previous related categories may be useful. We develop a new method for transfer le...
In the discrete filtering problem, a data sequence over a finite alphabet is assumed to be corrupted by a discrete memoryless channel. The goal is to reconstruct the clean sequenc...
Erik Ordentlich, Tsachy Weissman, Marcelo J. Weinb...
Leveraging the power of nowadays graphics processing units for robust power grid simulation remains a challenging task. Existing preconditioned iterative methods that require inco...
Regulations in various countries permit the reuse of health information without patient authorization provided the data is "de-identified". In the United States, for ins...
In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for...