Regina Barzilay

Regina Barzilay (born 1970) is an Israeli-American computer scientist. She is a professor at the Massachusetts Institute of Technology and a faculty lead for artificial intelligence at the MIT Jameel Clinic. Her research interests are in natural language processing and applications of deep learning to chemistry and oncology. Provided by Wikipedia
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  1. 1
    Probabilistic approaches for modeling text structure and their application to text-to-text generation
    by Barzilay, Regina
    Published 2012
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  2. 2
    Hierarchical Low-Rank Tensors for Multilingual Transfer Parsing
    by Barzilay, Regina, Zhang, Yuan
    Published 2017
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  3. 3
    Using Semantic Unification to Generate Regular Expressions from Natural Language
    by Kushman, Nate, Barzilay, Regina
    Published 2013
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  4. 4
    Automatic Aggregation by Joint Modeling of Aspects and Values
    by Sauper, Christina, Barzilay, Regina
    Published 2013
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  5. 5
    Climbing the tower of babel: Unsupervised multilingual learning
    by Snyder, Benjamin, Barzilay, Regina
    Published 2011
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  6. 6
    Using semantic cues to learn syntax
    by Naseem, Tahira, Barzilay, Regina
    Published 2012
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  7. 7
    Automatically Generating Wikipedia Articles: A Structure-Aware Approach
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  8. 8
    Selective Sharing for Multilingual Dependency Parsing
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  9. 9
    Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence
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  10. 10
    Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B
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  11. 11
    Multi-Source Domain Adaptation with Mixture of Experts
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  12. 12
    Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B
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  13. 13
    Transfer Learning for Constituency-Based Grammars
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  14. 14
    Discourse Topic and Gestural Form
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  15. 15
    Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers
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  16. 16
    Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers
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  17. 17
    Junction tree variational autoencoder for molecular graph generation
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  18. 18
    Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers
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  19. 19
    Molding CNNs for text: Non-linear, non-consecutive convolutions
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  20. 20
    Rationalizing Neural Predictions
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