Hung Q. Ngo
About
In The Last Decade
Hung Q. Ngo
81 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 118
- Artificial Intelligence 606
- Computer Networks and Communications 535
- Signal Processing 367
- Computer Vision and Pattern Recognition 312
- Information Systems 233
Countries citing papers authored by Hung Q. Ngo
This map shows the geographic impact of Hung Q. Ngo'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 Hung Q. Ngo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hung Q. Ngo more than expected).
Fields of papers citing papers by Hung Q. Ngo
This network shows the impact of papers produced by Hung Q. Ngo. 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 Hung Q. Ngo. The network helps show where Hung Q. Ngo may publish in the future.
Co-authorship network of co-authors of Hung Q. Ngo
This figure shows the co-authorship network connecting the top 25 collaborators of Hung Q. Ngo. A scholar is included among the top collaborators of Hung Q. Ngo 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 Hung Q. Ngo. Hung Q. Ngo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | Learning models over relational data using sparse tensors and functional dependencies | 27 |
| 4 | 8 | |
| 5 | 9 | |
| 6 | 6 | |
| 7 | In-Database Learning with Sparse Tensors | 3 |
| 8 | In-Database Factorized Learning. | 1 |
| 9 | 5 | |
| 10 | A Resolution-based Framework for Joins: Worst-case and Beyond | 1 |
| 11 | 6 | |
| 12 | 9 | |
| 13 | 32 | |
| 14 | Upper confidence weighted learning for efficient exploration in multiclass prediction with binary feedback | 3 |
| 15 | Removing the Haystack to Find the Needle(s): Minesweeper, an adaptive join algorithm | 1 |
| 16 | Towards Instance Optimal Join Algorithms for Data in Indexes | 1 |
| 17 | Automatic Searching for English-Vietnamese Documents on the Internet | 2 |
| 18 | 43 | |
| 19 | Insider abuse comprehension through capability acquisition graphs | 8 |
| 20 | 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.