Tzu-Kuo Huang
- Automotive Engineering top 2%
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 2%
- Building and Construction top 5%
- Co-authors
- Jeff SchneiderLiang XiongJaime G. CarbonellXi ChenNemanja DjuricVladan RadosavljevićFang‐Chieh ChouTsung-Han Lin
- Topics
- Machine Learning and Algorithms (4 papers)Control Systems and Identification (3 papers)Gaussian Processes and Bayesian Inference (3 papers)
- Journals
- FigshareNeural Information Processing SystemsInternational Conference on Machine Learning
- Partner nations
- United StatesIndia
In The Last Decade
Tzu-Kuo Huang
10 papers receiving 863 citations
Hit Papers
Peers
Comparison fields: 5 of 73
- Automotive Engineering 349
- Artificial Intelligence 313
- Computer Vision and Pattern Recognition 280
- Information Systems 269
- Building and Construction 141
Countries citing papers authored by Tzu-Kuo Huang
This map shows the geographic impact of Tzu-Kuo Huang'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 Tzu-Kuo Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tzu-Kuo Huang more than expected).
Fields of papers citing papers by Tzu-Kuo Huang
This network shows the impact of papers produced by Tzu-Kuo Huang. 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 Tzu-Kuo Huang. The network helps show where Tzu-Kuo Huang may publish in the future.
Co-authorship network of co-authors of Tzu-Kuo Huang
This figure shows the co-authorship network connecting the top 25 collaborators of Tzu-Kuo Huang. A scholar is included among the top collaborators of Tzu-Kuo Huang 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 Tzu-Kuo Huang. Tzu-Kuo Huang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networksbreakdown → | 414 |
| 3 | Active search and bandits on graphs using sigma-optimality | 14 |
| 4 | Active Transfer Learning under Model Shift | 30 |
| 5 | Spectral Learning of Hidden Markov Models from Dynamic and Static Data | 1 |
| 6 | Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition | 1 |
| 7 | Learning Auto-regressive Models from Sequence and Non-sequence Data | 8 |
| 8 | Learning Nonlinear Dynamic Models from Non-sequenced Data | 3 |
| 9 | Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorizationbreakdown → | 410 |
| 10 | 4 |
About Tzu-Kuo Huang
Tzu-Kuo Huang is a scholar working on Computational Mathematics, Artificial Intelligence and Signal Processing, having authored 10 papers that have together received 893 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (4 papers), Control Systems and Identification (3 papers) and Gaussian Processes and Bayesian Inference (3 papers). The work is most often cited by research in Computational Mathematics (119 citations), Automotive Engineering (349 citations) and Transportation (93 citations). Tzu-Kuo Huang has collaborated with scholars based in United States and India. Frequent co-authors include Jeff Schneider, Liang Xiong, Jaime G. Carbonell, Xi Chen, Nemanja Djuric, Vladan Radosavljević, Fang‐Chieh Chou, Tsung-Han Lin, Thi Nguyen and Henggang Cui. Their work appears in journals such as Figshare, Neural Information Processing Systems and International Conference on Machine Learning.
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.