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Computer Science

D-Index
95
Citations
41752
World Ranking
459
National Ranking
252

Overview

Raymond J. Mooney is affiliated with The University of Texas at Austin in the United States. Their research contributions are primarily situated within the field of Computer Science, with a significant focus on subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Control and Systems Engineering, and Software.

The research topics covered by Raymond J. Mooney span several areas including:

  • Multimodal Machine Learning Applications
  • Topic Modeling
  • Natural Language Processing Techniques
  • Software Engineering Research
  • Speech and dialogue systems
  • Domain Adaptation and Few-Shot Learning
  • Advanced Image and Video Retrieval Techniques

Their publication record includes a mix of journal articles, conference papers, and preprints, with frequent venues including arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence, Computer Speech & Language, and the Journal of Artificial Intelligence Research.

Some recent papers by Raymond J. Mooney include:

  • Spoken language interaction with robots: Recommendations for future research, 2021, Computer Speech & Language
  • Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog, 2020, Journal of Artificial Intelligence Research
  • PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards, 2020, arXiv (Cornell University)
  • Improving VQA and its Explanations by Comparing Competing Explanations, 2020, arXiv (Cornell University)
  • Sparse Meets Dense: A Hybrid Approach to Enhance Scientific Document Retrieval, 2024, arXiv (Cornell University)

They have collaborated frequently with several co-authors, most notably:

  • Milos Gligoric
  • Junyi Jessy Li
  • Sheena Panthaplackel
  • Stefanie Tellex
  • Matthew Marge

This body of work illustrates an emphasis on integrating language understanding with machine learning techniques, as well as interdisciplinary applications involving speech, vision, and robotics. The diversity of research topics and publication venues reflects a broad engagement with multiple aspects of artificial intelligence and computer science.

Best Publications

  • Content-based book recommending using learning for text categorization

    Raymond J. Mooney;Loriene Roy

  • Sequence to Sequence -- Video to Text

    Subhashini Venugopalan;Marcus Rohrbach;Jeffrey Donahue;Raymond Mooney

  • Explanation-Based Learning: An Alternative View

    Gerald Dejong;Raymond Mooney

  • Adaptive duplicate detection using learnable string similarity measures

    Mikhail Bilenko;Raymond J. Mooney

  • Semi-supervised Clustering by Seeding

    Sugato Basu;Arindam Banerjee;Raymond J. Mooney

  • A Shortest Path Dependency Kernel for Relation Extraction

    Razvan Bunescu;Raymond Mooney

  • Integrating constraints and metric learning in semi-supervised clustering

    Mikhail Bilenko;Sugato Basu;Raymond J. Mooney

  • A probabilistic framework for semi-supervised clustering

    Sugato Basu;Mikhail Bilenko;Raymond J. Mooney

  • Impact of Similarity Measures on Web-page Clustering

    Alexander Strehl;Joydeep Ghosh;Raymond Mooney

  • Translating Videos to Natural Language Using Deep Recurrent Neural Networks

    Subhashini Venugopalan;Huijuan Xu;Jeff Donahue;Marcus Rohrbach

  • Semi-supervised graph clustering: a kernel approach

    Brian Kulis;Sugato Basu;Inderjit Dhillon;Raymond Mooney

  • Active Semi-Supervision for Pairwise Constrained Clustering

    Sugato Basu;Arindam Banerjee;Raymond J. Mooney

  • Relational learning of pattern-match rules for information extraction

    Mary Elaine Califf;Raymond J. Mooney

  • Learning to parse database queries using inductive logic programming

    John M. Zelle;Raymond J. Mooney

  • Adaptive name matching in information integration

    M. Bilenko;R. Mooney;W. Cohen;P. Ravikumar

  • Subsequence Kernels for Relation Extraction

    Raymond J. Mooney;Razvan C. Bunescu

  • YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-Shot Recognition

    Sergio Guadarrama;Niveda Krishnamoorthy;Girish Malkarnenkar;Subhashini Venugopalan

  • Comparative experiments on learning information extractors for proteins and their interactions

    Razvan Bunescu;Ruifang Ge;Rohit J. Kate;Edward M. Marcotte

  • Learning to interpret natural language navigation instructions from observations

    David L. Chen;Raymond J. Mooney

  • Symbolic and neural learning algorithms: an experimental comparison

    Jude W. Shavlik;Raymond J. Mooney;Geoffrey G. Towell

Frequent Co-Authors

Kate Saenko
Kate Saenko Boston University
Razvan Bunescu
Razvan Bunescu University of North Carolina at Charlotte
Peter Stone
Peter Stone The University of Texas at Austin
Katrin Erk
Katrin Erk The University of Texas at Austin
Marcus Rohrbach
Marcus Rohrbach Facebook (United States)
Trevor Darrell
Trevor Darrell University of California, Berkeley
Hwee Tou Ng
Hwee Tou Ng National University of Singapore
Joydeep Ghosh
Joydeep Ghosh The University of Texas at Austin
Jude W. Shavlik
Jude W. Shavlik University of Wisconsin–Madison
Edward M. Marcotte
Edward M. Marcotte The University of Texas at Austin

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