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Blocking is a technique commonly used in manual statistical analysis to account for confounding variables. However, blocking is not currently used in automated learning algorithms...
— Recent advances in machine learning and adaptive motor control have enabled efficient techniques for online learning of stationary plant dynamics and it’s use for robust pre...
We present a novel probabilistic multiple cause model for binary observations. In contrast to other approaches, the model is linear and it infers reasons behind both observed and ...
We derive two variants of a semi-supervised model for fine-grained sentiment analysis. Both models leverage abundant natural supervision in the form of review ratings, as well as...