David I. Inouye
- Artificial Intelligence top 10%
- Information Systems top 10%
- Statistical and Nonlinear Physics top 10%
- Computer Vision and Pattern Recognition
- Signal Processing
- Co-authors
- Jugal KalitaPradeep RavikumarInderjit S. DhillonSilvia S. BlemkerCheng-Yu HsiehChih‐Kuan YehGenevera I. AllenEunho Yang
- Topics
- Topic Modeling (4 papers)Natural Language Processing Techniques (3 papers)Bayesian Methods and Mixture Models (2 papers)
- Journals
- The Journal of the Acoustical Society of AmericaThe Computer JournalWiley Interdisciplinary Reviews Computational Statistics
- Partner nations
- United StatesSouth Korea
In The Last Decade
David I. Inouye
13 papers receiving 195 citations
Peers
Comparison fields: 5 of 47
- Artificial Intelligence 147
- Information Systems 70
- Statistical and Nonlinear Physics 54
- Computer Vision and Pattern Recognition 18
- Signal Processing 14
Countries citing papers authored by David I. Inouye
This map shows the geographic impact of David I. Inouye'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 David I. Inouye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David I. Inouye more than expected).
Fields of papers citing papers by David I. Inouye
This network shows the impact of papers produced by David I. Inouye. 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 David I. Inouye. The network helps show where David I. Inouye may publish in the future.
Co-authorship network of co-authors of David I. Inouye
This figure shows the co-authorship network connecting the top 25 collaborators of David I. Inouye. A scholar is included among the top collaborators of David I. Inouye 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 David I. Inouye. David I. Inouye 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 | 2 | |
| 3 | 1 | |
| 4 | Diagnostic Curves for Black Box Models. | 1 |
| 5 | How Sensitive are Sensitivity-Based Explanations? | 5 |
| 6 | 3 | |
| 7 | Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies. | 9 |
| 8 | Fixed-length Poisson MRF: adding dependencies to the Multinomial | 3 |
| 9 | Admixture of Poisson MRFs: A Topic Model with Word Dependencies | 14 |
| 10 | Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs | 4 |
| 11 | 12 | |
| 12 | 25 | |
| 13 | VisIRR: Interactive Visual Information Retrieval and Recommendation for Large-scale Document Data | 6 |
| 14 | 119 |
About David I. Inouye
David I. Inouye is a scholar working on Artificial Intelligence, History and Philosophy of Science and Signal Processing, having authored 14 papers that have together received 204 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Natural Language Processing Techniques (3 papers) and Bayesian Methods and Mixture Models (2 papers). The work is most often cited by research in Artificial Intelligence (147 citations), Statistical and Nonlinear Physics (54 citations) and Information Systems (70 citations). David I. Inouye has collaborated with scholars based in United States and South Korea. Frequent co-authors include Jugal Kalita, Pradeep Ravikumar, Inderjit S. Dhillon, Silvia S. Blemker, Cheng-Yu Hsieh, Pradeep Ravikumar, Chih‐Kuan Yeh, Genevera I. Allen, Eunho Yang and Saurabh Bagchi. Their work appears in journals such as The Journal of the Acoustical Society of America, The Computer Journal and Wiley Interdisciplinary Reviews Computational Statistics.
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.