Gavin Taylor
Impact in
- Artificial Intelligence top 5%
- Reinforcement Learning in Robotics
- Adversarial Robustness in Machine Learning
- Domain Adaptation and Few-Shot Learning
- Stochastic Gradient Optimization Techniques
- Neural Networks and Applications
- Advanced Graph Neural Networks
Papers in
-
- Adversarial Robustness in Machine Learning 4
- Reinforcement Learning in Robotics 3
- Machine Learning and ELM 2
- Neural Networks and Applications 2
- Stochastic Gradient Optimization Techniques 2
- Co-authors
- Tom GoldsteinRonald ParrZheng XuChristoph StuderHao LiMichael L. LittmanLihong LiAnkit Patel
- Journals
- Systems Engineering (1 paper)Sensors (1 paper)Future Generation Computer Systems (1 paper)Cryptologia (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)
- Partner nations
- United StatesNorwayCanada
In The Last Decade
Gavin Taylor
16 papers receiving 556 citations
Peers
Comparison fields: 5 of 97
- Artificial Intelligence 356
- Computational Mathematics 4
- Computer Vision and Pattern Recognition 137
- Computational Theory and Mathematics 67
- Management Science and Operations Research 46
Countries citing papers authored by Gavin Taylor
This map shows the geographic impact of Gavin Taylor'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 Gavin Taylor with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gavin Taylor more than expected).
Fields of papers citing papers by Gavin Taylor
This network shows the impact of papers produced by Gavin Taylor. 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 Gavin Taylor. The network helps show where Gavin Taylor may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Gavin Taylor, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 0 | |
| 2 | 2023 | 5 | |
| 3 | 2022 | 50 | |
| 4 | LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition | 2021 | 38 |
| 5 | 2021 | 11 | |
| 6 | MetaPoison: Practical General-purpose Clean-label Data Poisoning | 2020 | 3 |
| 7 | 2019 | 5 | |
| 8 | 2019 | 8 | |
| 9 | 2018 | 1 | |
| 10 | 2018 | 167 | |
| 11 | Adaptive Consensus ADMM for Distributed Optimization | 2017 | 8 |
| 12 | Introduction to the Symposium on AI and the Mitigation of Human Error | 2016 | 1 |
| 13 | 2016 | 7 | |
| 14 | 2016 | 56 | |
| 15 | 2010 | 28 | |
| 16 | 2009 | 55 | |
| 17 | 2008 | 74 | |
| 18 | Exploration Strategy Workshop | 2006 | 1 |
| 19 | Neural Networks and Their Applications | 1996 | 76 |
About Gavin Taylor
Gavin Taylor is a scholar working on Health Informatics, Artificial Intelligence, Computational Theory and Mathematics, Computer Science Applications and Statistical and Nonlinear Physics, having authored 19 papers that have together received 594 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (4 papers), Reinforcement Learning in Robotics (3 papers), Advanced Multi-Objective Optimization Algorithms (2 papers), Machine Learning and ELM (2 papers), Neural Networks and Applications (2 papers), Stochastic Gradient Optimization Techniques (2 papers), Modular Robots and Swarm Intelligence (1 paper) and Complex Systems and Decision Making (1 paper). The work is most often cited by research in Artificial Intelligence (356 citations), Computational Mathematics (4 citations), Computer Vision and Pattern Recognition (137 citations), Computational Theory and Mathematics (67 citations) and Management Science and Operations Research (46 citations). Gavin Taylor has collaborated with scholars based in United States, Norway and Canada. Frequent co-authors include Tom Goldstein, Ronald Parr, Zheng Xu, Christoph Studer, Hao Li, Michael L. Littman, Lihong Li, Ankit Patel, Bharat Singh and Chen Zhu. Their work appears in journals such as Systems Engineering, Sensors, Future Generation Computer Systems, Cryptologia and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.