Tien-Ju Yang
- Electrical and Electronic Engineering top 2%
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 1%
- Hardware and Architecture top 1%
- Computer Networks and Communications top 2%
- Topics
- Advanced Neural Network Applications (10 papers)Advanced Memory and Neural Computing (6 papers)Privacy-Preserving Technologies in Data (3 papers)
- Journals
- Proceedings of the IEEEIEEE Journal on Emerging and Selected Topics in Circuits and SystemsIEEE Solid-State Circuits Magazine
- Partner nations
- United StatesTaiwan
In The Last Decade
Tien-Ju Yang
18 papers receiving 3.7k citations
Hit Papers
Peers
Comparison fields: 5 of 154
- Electrical and Electronic Engineering 1.8k
- Computer Vision and Pattern Recognition 1.7k
- Artificial Intelligence 1.4k
- Hardware and Architecture 494
- Computer Networks and Communications 449
Countries citing papers authored by Tien-Ju Yang
This map shows the geographic impact of Tien-Ju Yang'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 Tien-Ju Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tien-Ju Yang more than expected).
Fields of papers citing papers by Tien-Ju Yang
This network shows the impact of papers produced by Tien-Ju Yang. 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 Tien-Ju Yang. The network helps show where Tien-Ju Yang may publish in the future.
Co-authorship network of co-authors of Tien-Ju Yang
This figure shows the co-authorship network connecting the top 25 collaborators of Tien-Ju Yang. A scholar is included among the top collaborators of Tien-Ju Yang 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 Tien-Ju Yang. Tien-Ju Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 7 | |
| 3 | 10 | |
| 4 | 5 | |
| 5 | 17 | |
| 6 | 79 | |
| 7 | 83 | |
| 8 | 53 | |
| 9 | Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devicesbreakdown → | 689 |
| 10 | 36 | |
| 11 | 79 | |
| 12 | 159 | |
| 13 | 129 | |
| 14 | Efficient Processing of Deep Neural Networks: A Tutorial and Surveybreakdown → | 2452 |
| 15 | A 401GFlops/W 16-cores signal reconstruction platform with a 4G entries/s matrix generation engine for compressed sensing and sparse representation | 1 |
| 16 | 11 | |
| 17 | 2 | |
| 18 | 3 |
About Tien-Ju Yang
Tien-Ju Yang is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence, having authored 18 papers that have together received 3.8k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (10 papers), Advanced Memory and Neural Computing (6 papers) and Privacy-Preserving Technologies in Data (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.7k citations), Hardware and Architecture (494 citations) and Computational Mathematics (40 citations). Tien-Ju Yang has collaborated with scholars based in United States and Taiwan. Frequent co-authors include Vivienne Sze, Joel Emer, Yu‐Hsin Chen, Ariel Gordon, Bo Chen, Edward Choi, Ofir Nachum, Hao Wu, Elad Eban and Stella X. Yu. Their work appears in journals such as Proceedings of the IEEE, IEEE Journal on Emerging and Selected Topics in Circuits and Systems and IEEE Solid-State Circuits Magazine.
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.