Tractable Deep Learning
In machine learning, as throughout computer science, there is a tradeoff between expressiveness and tractability. On the one hand, we need powerful model classes to capture the richness and complexity of the real world. On the other, we need inference in those models to remain tractable, otherwise their potential for widespread practical use is limited. Deep learning can induce powerful representations, with multiple layers of latent variables, but these models are generally intractable. We are developing new classes of similarly expressive but still tractable models, including sum-product networks and tractable Markov logic. These models capture both class-subclass and part-subpart structure in the domain, and are in some aspects more expressive than traditional graphical models like Bayesian networks and Markov random fields. Research includes designing representations, studying their properties, developing efficient algorithms for learning them, and applications to challenging problems in natural language understanding, vision, and other areas.
Awards
- NIPS 2012 Outstanding Student Paper: Discriminative Learning of Sum-Product Networks
- UAI 2011 Best Paper: Sum-Product Networks: A New Deep Architecture
- EMNLP 2009 Best Paper: Unsupervised Semantic Parsing
People
- Pedro Domingos
- Abram L Friesen
- Robert C Gens
- Chloe M Kiddon
- Aniruddh Nath
- Mathias Niepert
- W Austin Webb
Publications
- Learning the Structure of Sum-Product Networks (2013)
- A Tractable First-Order Probabilistic Logic (2012)
- Discriminative Learning of Sum-Product Networks (2012)
- Learning Multiple Hierarchical Relational Clusterings (2012)
- Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models (2011)
- Sum-Product Networks: A New Deep Architecture (2011)
- Approximate Inference by Compilation to Arithmetic Circuits (2010)
- Learning Efficient Markov Networks (2010)
- Unsupervised Ontology Induction from Text (2010)
- Unsupervised Semantic Parsing (2009)
- Learning Arithmetic Circuits (2008)
- Naive Bayes Models for Probability Estimation (2005)
Research Groups
Last changed Wed, 2013-01-02 13:05

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