Dinh Phung
About
In The Last Decade
Dinh Phung
240 papers receiving 5.7k citations
Hit Papers
Peers
Comparison fields: 5 of 181
- Artificial Intelligence 2.8k
- Computer Vision and Pattern Recognition 1.5k
- Signal Processing 630
- Information Systems 559
- Computer Networks and Communications 521
Countries citing papers authored by Dinh Phung
This map shows the geographic impact of Dinh Phung'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 Dinh Phung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dinh Phung more than expected).
Fields of papers citing papers by Dinh Phung
This network shows the impact of papers produced by Dinh Phung. 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 Dinh Phung. The network helps show where Dinh Phung may publish in the future.
Co-authorship network of co-authors of Dinh Phung
This figure shows the co-authorship network connecting the top 25 collaborators of Dinh Phung. A scholar is included among the top collaborators of Dinh Phung 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 Dinh Phung. Dinh Phung 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 | 24 | |
| 4 | 1 | |
| 5 | Parameterized Rate-Distortion Stochastic Encoder | 1 |
| 6 | Universal Self-Attention Network for Graph Classification | 5 |
| 7 | Probabilistic Multilevel Clustering via Composite Transportation Distance | 3 |
| 8 | Clustering Induced Kernel Learning | 1 |
| 9 | Multilevel Clustering via Wasserstein Means | 8 |
| 10 | Supervised Restricted Boltzmann Machines. | 3 |
| 11 | Multiple Kernel Learning with Data Augmentation | 8 |
| 12 | Budgeted semi-supervised support vector machine | 11 |
| 13 | Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View breakdown → | 695 |
| 14 | Using Shannon Entropy as EEG Signal Feature for Fast Person Identification | 41 |
| 15 | Journal of Machine Learning Research: Preface | 10 |
| 16 | Learning Parts-based Representations with Nonnegative Restricted Boltzmann Machine | 17 |
| 17 | Factorial Multi-Task Learning : A Bayesian Nonparametric Approach | 15 |
| 18 | Multi-modal abnormality detection in video with unknown data segmentation | 3 |
| 19 | Learning From Ordered Sets and Applications in Collaborative Ranking | 3 |
| 20 | A nonparametric Bayesian Poisson Gamma model for count data | 8 |
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.