World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
9069
World Ranking
8697
National Ranking
3728

Overview

Matt Gardner is affiliated with the Allen Institute for Artificial Intelligence in the United States. Their research primarily focuses on computer science, with a significant volume of work in artificial intelligence and related subfields.

The main fields of study covered in Gardner's publications include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Epidemiology
  • Information Systems
  • Surgery

Gardner's research topics span a diverse range of areas within computational methods and machine learning, including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Text Readability and Simplification
  • Advanced Text Analysis Techniques
  • Domain Adaptation and Few-Shot Learning
  • Speech and Dialogue Systems

The scientist has published extensively in various venues, with frequent appearances in:

  • arXiv (Cornell University)
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • Transactions of the Association for Computational Linguistics
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Among recent publications involving Matt Gardner as an author are:

  • "Competency Problems: On Finding and Removing Artifacts in Language Data," 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • "Evaluating Models' Local Decision Boundaries via Contrast Sets," 2020, arXiv (Cornell University)

Other significant papers coauthored by Gardner with colleagues include:

  • "Break It Down: A Question Understanding Benchmark," 2020, Transactions of the Association for Computational Linguistics
  • "ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension," 2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • "Impact of Pretraining Term Frequencies on Few-Shot Reasoning," 2022, arXiv (Cornell University)

Frequent coauthors collaborating with Gardner include:

  • Sameer Singh
  • Pradeep Dasigi
  • Jonathan Berant
  • Dheeru Dua
  • Sanjay Subramanian

Best Publications

  • Deep contextualized word representations

    Matthew E. Peters;Mark Neumann;Mohit Iyyer;Matt Gardner

  • Deep contextualized word representations

    Matthew E. Peters;Mark Neumann;Mohit Iyyer;Matt Gardner

  • AllenNLP: A Deep Semantic Natural Language Processing Platform

    Matt Gardner;Joel Grus;Mark Neumann;Oyvind Tafjord

  • Never-ending learning

    T. Mitchell;W. Cohen;E. Hruschka;P. Talukdar

  • Never-ending learning

    T. Mitchell;W. Cohen;E. Hruschka;P. Talukdar

  • Linguistic Knowledge and Transferability of Contextual Representations

    Nelson F. Liu;Matt Gardner;Yonatan Belinkov;Matthew E. Peters

  • Universal Adversarial Triggers for Attacking and Analyzing NLP

    Eric Wallace;Shi Feng;Nikhil Kandpal;Matt Gardner

  • AllenNLP: A Deep Semantic Natural Language Processing Platform

    Matt Gardner;Joel Grus;Mark Neumann;Oyvind Tafjord

  • DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

    Dheeru Dua;Yizhong Wang;Pradeep Dasigi;Gabriel Stanovsky

  • Simple and Effective Multi-Paragraph Reading Comprehension

    Christopher Clark;Matt Gardner

  • Evaluating Models’ Local Decision Boundaries via Contrast Sets

    Matt Gardner;Yoav Artzi;Victoria Basmov;Jonathan Berant

  • Neural Semantic Parsing with Type Constraints for Semi-Structured Tables

    Jayant Krishnamurthy;Pradeep Dasigi;Matt Gardner

  • Do NLP Models Know Numbers? Probing Numeracy in Embeddings

    Eric Wallace;Yizhong Wang;Sujian Li;Sameer Singh

  • Efficient and Expressive Knowledge Base Completion Using Subgraph Feature Extraction

    Matt Gardner;Tom Mitchell

  • DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

    Unknown

  • Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases

    Matt Gardner;Partha Talukdar;Jayant Krishnamurthy;Tom Mitchell

  • Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling

    Robert L. Logan;Nelson F. Liu;Matthew E. Peters;Matt Gardner

  • Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing.

    Ben Bogin;Jonathan Berant;Matt Gardner

  • Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning

    Pradeep Dasigi;Nelson F. Liu;Ana Marasović;Noah A. Smith

  • Compositional Questions Do Not Necessitate Multi-hop Reasoning.

    Sewon Min;Eric Wallace;Sameer Singh;Matt Gardner

  • Crowdsourcing Multiple Choice Science Questions

    Johannes Welbl;Nelson F. Liu;Matt Gardner

  • AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models

    Eric Wallace;Jens Tuyls;Junlin Wang;Sanjay Subramanian

  • Improving Learning and Inference in a Large Knowledge-Base using Latent Syntactic Cues

    Matt Gardner;Partha Pratim Talukdar;Bryan Kisiel;Tom Mitchell

  • Break It Down: A Question Understanding Benchmark

    Tomer Wolfson;Tomer Wolfson;Mor Geva;Mor Geva;Ankit Gupta;Matt Gardner

  • Global Reasoning over Database Structures for Text-to-SQL Parsing

    Ben Bogin;Matt Gardner;Jonathan Berant

  • Reasoning Over Paragraph Effects in Situations

    Kevin Lin;Oyvind Tafjord;Peter Clark;Matt Gardner

  • Neural Module Networks for Reasoning over Text

    Nitish Gupta;Kevin Lin;Dan Roth;Sameer Singh

  • Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning

    Unknown

  • ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension

    Unknown

  • Evaluating Models' Local Decision Boundaries via Contrast Sets.

    Matt Gardner;Yoav Artzi;Victoria Basmova;Jonathan Berant

  • A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

    Pradeep Dasigi;Kyle Lo;Iz Beltagy;Arman Cohan

Frequent Co-Authors

Sameer Singh
Sameer Singh University of California, Irvine
Jonathan Berant
Jonathan Berant Tel Aviv University
Noah A. Smith
Noah A. Smith University of Washington
Hannaneh Hajishirzi
Hannaneh Hajishirzi University of Washington
Luke Zettlemoyer
Luke Zettlemoyer University of Washington
Tom M. Mitchell
Tom M. Mitchell Carnegie Mellon University
Partha Pratim Talukdar
Partha Pratim Talukdar Indian Institute of Science
Dan Roth
Dan Roth University of Pennsylvania
Anthony Chen
Anthony Chen Hong Kong Polytechnic University
Maarten Sap
Maarten Sap Carnegie Mellon University

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