We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradie...
Next to prediction accuracy, the interpretability of models is one of the fundamental criteria for machine learning algorithms. While high accuracy learners have intensively been e...
Learning Bayesian Belief Networks (BBN) from corpora and incorporating the extracted inferring knowledge with a Support Vector Machines (SVM) classifier has been applied to charac...
Named-entity recognition (NER) is an important task required in a wide variety of applications. While rule-based systems are appealing due to their well-known "explainability...
Laura Chiticariu, Rajasekar Krishnamurthy, Yunyao ...
Solving in an efficient manner many different optimal control tasks within the same underlying environment requires decomposing the environment into its computationally elemental ...