Jake Grigsby

717 citations
2 papers · 313 · 1 hit paper · h-index 2

Impact in

    • Topic Modeling
    • Adversarial Robustness in Machine Learning
    • Natural Language Processing Techniques
    • Hate Speech and Cyberbullying Detection
    • Anomaly Detection Techniques and Applications
    • Explainable Artificial Intelligence (XAI)
    • Advanced Malware Detection Techniques

Papers in

Jake Grigsby

2 papers receiving 302 citations

Jake Grigsby's Hit Papers

TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP 2020 · 302 citations
3020+2+4Years since publication100200300

Peers

Jake Grigsby
Comparison fields: 5 of 48
  • Artificial Intelligence 268
  • Signal Processing 66
  • Health Informatics 5
  • Software 8
  • Information Systems 37
Replace Jin Yong Yoo with:
Jin Yong Yoo United States
Yihe Deng China
Jiazhu Dai China
Oliver Eberle Germany
Andrej Risteski United States
Arun Tejasvi Chaganty United States
Giannis Nikolentzos France
Luke Valenta United States
Walter Rudametkin France
Emilia Käsper United States
Jake Grigsby relative to Jin Yong Yoo United States Jin Yong Yoo's profile →
Citations per field
00.5×1.5×
Jin Yong Yoo · 1×
Citations per year

Countries citing papers authored by Jake Grigsby

Since Specialization
Citations

This map shows the geographic impact of Jake Grigsby's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jake Grigsby with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jake Grigsby more than expected).

Fields of papers citing papers by Jake Grigsby

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jake Grigsby. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jake Grigsby. The network helps show where Jake Grigsby may publish in the future.

Co-authors

The 9 scholars most cited alongside Jake Grigsby, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jake Grigsby Line = papers co-authored together Jake Grigsby links everyone, so they are left out of the graph.

All Works

2 of 2 papers shown
#Work
1
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP
Hit paper breakdown →
2020302
2 202111

About Jake Grigsby

Jake Grigsby is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Infectious Diseases and Organic Chemistry, having authored 2 papers that have together received 313 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (1 paper), Topic Modeling (1 paper), Advanced Malware Detection Techniques (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Advanced Neural Network Applications (1 paper) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Artificial Intelligence (268 citations), Signal Processing (66 citations), Health Informatics (5 citations), Software (8 citations) and Information Systems (37 citations). Jake Grigsby has collaborated with scholars based in United States. Frequent co-authors include Yanjun Qi, John X. Morris, Di Jin, Eli Lifland, Jin Yong Yoo, J. R. Hoskins, Simonetta Liuti, L. P. Alonzi and Matthias Burkardt. Their work appears in journals such as Physical review. D.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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