We present an approximate analytical method to compute efficiently the call blocking probabilities in wavelength routing networks with multiple classes of calls. The model is fairl...
Sridhar Ramesh, George N. Rouskas, Harry G. Perros
We present a probabilistic generative model for learning semantic parsers from ambiguous supervision. Our approach learns from natural language sentences paired with world states ...
We propose and study algorithms for computing minimal models, stable models and answer sets of 2- and 3-CNF theories, and normal and disjunctive 2- and 3-programs. We are especiall...
Retrieving images in response to textual queries requires some knowledge of the semantics of the picture. Here, we show how we can do both automatic image annotation and retrieval...
In this paper, we propose a new Bayesian model for fully unsupervised word segmentation and an efficient blocked Gibbs sampler combined with dynamic programming for inference. Our...