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个人简介
My research is on developing nonparametric, contextual reasoning models for program synthesis and question answering on knowledge graphs and text. These models are accurate, controllable (debuggable), offer interpretable predictions and can seamlessly reason with newly arriving information. Recently, I have been working on neuro-symbolic advancements to an old nonparametric framework initially proposed in classical AI - Case-based Reasoning. In a CBR framework, the reasoning pattern required to solve a problem are derived from the reasoning patterns of other similar problems. A CBR framework provides a natural way of extending K-nearest neighbor approaches for classification to more complex problems such as program synthesis and question answering. My research interests also include developing models for open-domain QA, building procedural knowledge graphs from text and common-sense reasoning.
研究兴趣
论文共 34 篇作者统计合作学者相似作者
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CoRR (2023): 8414-8428
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Rajarshi Das,Ameya Godbole, Ankita Rajaram Naik,Elliot Tower,Manzil Zaheer,Hannaneh Hajishirzi,Robin Jia,Andrew McCallum
ICML 2022 (2022)
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International Conference on Machine Learning (2022): 4777-4793
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Neha Nayak Kennard,Tim O'Gorman, Akshay Sharma, Chhandak Bagchi, Matthew Clinton, Pranay Kumar Yelugam,Rajarshi Das,Hamed Zamani,Andrew McCallum
NAACL 2022: THE 2022 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (2021): 1234-1249
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