John V. Guttag
About
In The Last Decade
John V. Guttag
136 papers receiving 7.5k citations
Hit Papers
Peers
Comparison fields: 5 of 186
- Artificial Intelligence 2.9k
- Computer Networks and Communications 2.2k
- Information Systems 1.7k
- Biomedical Engineering 1.4k
- Computational Theory and Mathematics 1.2k
Countries citing papers authored by John V. Guttag
This map shows the geographic impact of John V. Guttag'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 John V. Guttag with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John V. Guttag more than expected).
Fields of papers citing papers by John V. Guttag
This network shows the impact of papers produced by John V. Guttag. 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 John V. Guttag. The network helps show where John V. Guttag may publish in the future.
Co-authorship network of co-authors of John V. Guttag
This figure shows the co-authorship network connecting the top 25 collaborators of John V. Guttag. A scholar is included among the top collaborators of John V. Guttag based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with John V. Guttag. John V. Guttag is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 7 | |
| 3 | 2 | |
| 4 | 3 | |
| 5 | 9 | |
| 6 | 11 | |
| 7 | Learning Conditional Deformable Templates with Convolutional Networks | 10 |
| 8 | Learning to Summarize Electronic Health Records Using Cross-Modality Correspondences. | 3 |
| 9 | A Video-Based Method for Automatically Rating Ataxia | 6 |
| 10 | 74 | |
| 11 | Learning to Detect Vocal Hyperfunction From Ambulatory Neck-Surface Acceleration Features: Initial Results for Vocal Fold Nodules | 3 |
| 12 | Learning Connections in Financial Time Series | 14 |
| 13 | Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task | 55 |
| 14 | Motif Discovery in Physiological Datasets: A Methodology for Inferring Predictive Elements | 3 |
| 15 | Identifying Patients at Risk of Major Adverse Cardiovascular Events Using Symbolic Mismatch | 6 |
| 16 | Learning Approximate Sequential Patterns for Classification | 7 |
| 17 | Demonstration of SMART (Scalable Medical Alert Response Technology) | 4 |
| 18 | 3 | |
| 19 | An Introduction to the Larch Shared Language. | 17 |
| 20 | 65 |
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.