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Keynote: Marginalization-Based Parameter Learning in Graphical ModelsLikelihood-based learning of graphical models faces challenges of computational complexity and robustness to model error. This talk will discuss methods ...
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On Information-Maximization Clustering: Tuning Parameter Selection and Analytic SolutionInformation-maximization clustering learns a probabilistic classifier in an unsupervised manner so that mutual information between feature vectors and cluster assignments ...
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Opening Remarks
Opening remarks by the workshop organizers (Michael Wick, David Weiss, Sameer Singh and Andrew McCallum)
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Stacked Progressive Auto-Encoders (SPAE) for Face Recognition Across Poses
Identifying subjects with variations caused by poses is one of the most challenging tasks in face recognition, since the difference ...
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Learning Structured Predictors (Part 2)Learning Structured Predictors continued.
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Scaling Deep LearningWe have recently started investigating how to scale deep learning techniques to much larger models in an effort to improve ...
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