James T. Kwok
About
In The Last Decade
James T. Kwok
212 papers receiving 9.7k citations
Hit Papers
Peers
Comparison fields: 5 of 182
- Artificial Intelligence 5.5k
- Computer Vision and Pattern Recognition 4.5k
- Media Technology 1.5k
- Signal Processing 1.1k
- Computational Mechanics 949
Countries citing papers authored by James T. Kwok
This map shows the geographic impact of James T. Kwok'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 James T. Kwok with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James T. Kwok more than expected).
Fields of papers citing papers by James T. Kwok
This network shows the impact of papers produced by James T. Kwok. 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 James T. Kwok. The network helps show where James T. Kwok may publish in the future.
Co-authorship network of co-authors of James T. Kwok
This figure shows the co-authorship network connecting the top 25 collaborators of James T. Kwok. A scholar is included among the top collaborators of James T. Kwok 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 James T. Kwok. James T. Kwok 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 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 3 | |
| 6 | 4 | |
| 7 | 10 | |
| 8 | 17 | |
| 9 | 5 | |
| 10 | Analysis of Quantized Models | 10 |
| 11 | Loss-aware Weight Quantization of Deep Networks. | 19 |
| 12 | Learning to predict from crowdsourced data | 31 |
| 13 | 110 | |
| 14 | Tighter and convex maximum margin clustering | 84 |
| 15 | Accelerated Gradient Methods for Stochastic Optimization and Online Learning | 65 |
| 16 | Ensembles of partially trained SWMs with multiplicative updates | 1 |
| 17 | 10 | |
| 18 | Learning with idealized kernels | 112 |
| 19 | Linear Dependency between epsilon and the Input Noise in epsilon-Support Vector Regression | 22 |
| 20 | Integrating the evidence framework and the support vector machine | 12 |
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.