Tien-Ju Yang

7.7k total citations · 2 hit papers
18 papers, 3.8k citations indexed

About

Tien-Ju Yang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Tien-Ju Yang has authored 18 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 9 papers in Artificial Intelligence and 7 papers in Electrical and Electronic Engineering. Recurrent topics in Tien-Ju Yang's work include Advanced Neural Network Applications (10 papers), Advanced Memory and Neural Computing (6 papers) and Privacy-Preserving Technologies in Data (3 papers). Tien-Ju Yang is often cited by papers focused on Advanced Neural Network Applications (10 papers), Advanced Memory and Neural Computing (6 papers) and Privacy-Preserving Technologies in Data (3 papers). Tien-Ju Yang collaborates with scholars based in United States and Taiwan. Tien-Ju Yang's 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 and has published in prestigious journals such as Proceedings of the IEEE, IEEE Journal on Emerging and Selected Topics in Circuits and Systems and IEEE Solid-State Circuits Magazine.

In The Last Decade

Tien-Ju Yang

18 papers receiving 3.7k citations

Hit Papers

Efficient Processing of Deep Neural Networks: A Tutorial ... 2017 2026 2020 2023 2017 2019 500 1000 1.5k 2.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Efficient Processing of Deep Neural Networks: A Tutorial and Survey
    2017 2.5k citations Vivienne Sze, Yu‐Hsin Chen et al. Proceedings of the IEEE profile →
  2. Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices
    2019 689 citations Tien-Ju Yang, Joel Emer et al. IEEE Journal on Emerging and Selected Topics in Circuits and Systems profile →

Peers

Tien-Ju Yang
Shouyi Yin China
Philip H. W. Leong Australia
Clément Farabet United States
Jinjun Xiong United States
Fan Yang China
Song Han United States
Tarek M. Taha United States
Marian Verhelst Belgium
Bei Yu Hong Kong
Kaisheng Ma China
Shouyi Yin China
Tien-Ju Yang
Citations per year, relative to Tien-Ju Yang Tien-Ju Yang (= 1×) peers Shouyi Yin

Countries citing papers authored by Tien-Ju Yang

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

18 of 18 papers shown
1.
Yang, Tien-Ju, Yonghui Xiao, Giovanni Motta, et al.. (2023). Online Model Compression for Federated Learning with Large Models. 1–5. 6 indexed citations
2.
Yang, Tien-Ju, et al.. (2022). Enabling On-Device Training of Speech Recognition Models With Federated Dropout. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 8757–8761. 7 indexed citations
3.
Yang, Tien-Ju, et al.. (2022). Partial Variable Training for Efficient on-Device Federated Learning. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 4348–4352. 10 indexed citations
4.
Xiao, Yonghui, Tien-Ju Yang, Ding Zhao, et al.. (2022). Federated Pruning: Improving Neural Network Efficiency with Federated Learning. Interspeech 2022. 1701–1705. 5 indexed citations
5.
6.
Sze, Vivienne, Yu‐Hsin Chen, Tien-Ju Yang, & Joel Emer. (2020). Efficient Processing of Deep Neural Networks. 79 indexed citations
7.
Sze, Vivienne, Yu‐Hsin Chen, Tien-Ju Yang, & Joel Emer. (2020). Efficient Processing of Deep Neural Networks. 15(2). 1–341. 83 indexed citations
8.
Sze, Vivienne, Yu‐Hsin Chen, Tien-Ju Yang, & Joel Emer. (2020). How to Evaluate Deep Neural Network Processors: TOPS/W (Alone) Considered Harmful. IEEE Solid-State Circuits Magazine. 12(3). 28–41. 53 indexed citations
9.
Yang, Tien-Ju, et al.. (2019). Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 9(2). 292–308. 689 indexed citations breakdown →
10.
Yang, Tien-Ju & Vivienne Sze. (2019). Design Considerations for Efficient Deep Neural Networks on Processing-in-Memory Accelerators. 22.1.1–22.1.4. 36 indexed citations
11.
Hwang, Jyh-Jing, Stella X. Yu, Jianbo Shi, et al.. (2019). SegSort: Segmentation by Discriminative Sorting of Segments. 7333–7343. 79 indexed citations
12.
Gordon, Ariel, Elad Eban, Ofir Nachum, et al.. (2018). MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks. 1586–1595. 159 indexed citations
13.
Yang, Tien-Ju, Yu‐Hsin Chen, Joel Emer, & Vivienne Sze. (2017). A method to estimate the energy consumption of deep neural networks. 1916–1920. 129 indexed citations
14.
Sze, Vivienne, Yu‐Hsin Chen, Tien-Ju Yang, & Joel Emer. (2017). Efficient Processing of Deep Neural Networks: A Tutorial and Survey. Proceedings of the IEEE. 105(12). 2295–2329. 2452 indexed citations breakdown →
15.
Tsai, Yi‐Min, Tien-Ju Yang, & Liang‐Gee Chen. (2013). A 401GFlops/W 16-cores signal reconstruction platform with a 4G entries/s matrix generation engine for compressed sensing and sparse representation. 1 indexed citations
16.
Tsai, Yi‐Min, Tien-Ju Yang, Chih-Chung Tsai, Keng‐Yen Huang, & Liang‐Gee Chen. (2012). A 69mW 140-meter/60fps and 60-meter/300fps intelligent vision SoC for versatile automotive applications. 152–153. 11 indexed citations
17.
Yang, Tien-Ju, Yi‐Min Tsai, Chung‐Te Li, & Liang‐Gee Chen. (2012). WarmL1: A warm-start homotopy-based reconstruction algorithm for sparse signals. 54. 2226–2230. 2 indexed citations
18.
Yang, Tien-Ju, Yi‐Min Tsai, & Liang‐Gee Chen. (2011). Smart display: A mobile self-adaptive projector-camera system. 1–6. 3 indexed citations

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.

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