Ji Liu

5.0k total citations · 1 hit paper
74 papers, 2.4k citations indexed

About

Ji Liu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Ji Liu has authored 74 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Artificial Intelligence, 31 papers in Computer Vision and Pattern Recognition and 7 papers in Computer Networks and Communications. Recurrent topics in Ji Liu's work include Machine Learning and Data Classification (11 papers), Stochastic Gradient Optimization Techniques (10 papers) and Advanced Neural Network Applications (8 papers). Ji Liu is often cited by papers focused on Machine Learning and Data Classification (11 papers), Stochastic Gradient Optimization Techniques (10 papers) and Advanced Neural Network Applications (8 papers). Ji Liu collaborates with scholars based in China, United States and Switzerland. Ji Liu's co-authors include Yang Cong, Junsong Yuan, Xiangru Lian, Ce Zhang, Jiang‐Ning Zhou, Gan Sun, Cho‐Jui Hsieh, Huan Zhang, Wei Zhang and Lingjuan Lyu and has published in prestigious journals such as Nature Communications, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Image Processing.

In The Last Decade

Ji Liu

70 papers receiving 2.3k citations

Hit Papers

align trajectories

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.

Sparse reconstruction cost for abnormal event detection

2011 581 citations Yang Cong, Junsong Yuan et al. profile →

Peers

Ji Liu

AI

CVPR

CNC

BE

CM

Tao Song China

Si Wu China

Yi Xu China

Christin Schäfer Germany

Chiew Tong Lau Singapore

Tianyi Zhou United States

Preetha Phillips United States

Julio Ortega Spain

Muhammad Hussain Saudi Arabia

N. Sundararajan Singapore

Tao Song China

Ji Liu

541 ×0.4

AI

562 ×0.6

CVPR

308 ×0.5

CNC

142 ×0.7

BE

47 ×0.3

CM

Citations per year, relative to Ji Liu Ji Liu (= 1×) peers Tao Song

Countries citing papers authored by Ji Liu

Since Specialization

Citations

This map shows the geographic impact of Ji Liu'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 Ji Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ji Liu more than expected).

Fields of papers citing papers by Ji Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ji Liu. 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 Ji Liu. The network helps show where Ji Liu may publish in the future.

Co-authorship network of co-authors of Ji Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Ji Liu. A scholar is included among the top collaborators of Ji Liu 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 Ji Liu. Ji Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Guo, Yahui, Wei Zhou, Yongshuo H. Fu, et al.. (2025). SegNeXt-RCMSCA: An improved SegNeXt network for detecting winter wheat lodging from UAS RGB images. Smart Agricultural Technology. 12. 101230–101230. 2 indexed citations

2.

Qiu, Shuang, Binhang Yuan, Jiawei Jiang, et al.. (2024). Stochastic gradient descent without full data shuffle: with applications to in-database machine learning and deep learning systems. The VLDB Journal. 33(5). 1231–1255. 2 indexed citations

3.

Zhang, Chongsheng, Yaxin Hou, Ke Chen, et al.. (2023). Quality-Aware Self-Training on Differentiable Synthesis of Rare Relational Data. Proceedings of the AAAI Conference on Artificial Intelligence. 37(5). 6602–6611. 2 indexed citations

4.

Zhu, Haowei, et al.. (2022). Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 4682–4692. 135 indexed citations

5.

Conde, Kristie, Zhongzhong Li, Lei Yao, et al.. (2022). Glucose-sensing glucagon-like peptide-1 receptor neurons in the dorsomedial hypothalamus regulate glucose metabolism. Science Advances. 8(23). eabn5345–eabn5345. 44 indexed citations

6.

Dong, Wanying, Rui Hu, Wei Xiong, et al.. (2022). A neural circuit for the suppression of feeding under persistent pain. Nature Metabolism. 4(12). 1746–1755. 18 indexed citations

7.

Chen, Tianlong, et al.. (2020). Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free. arXiv (Cornell University). 33. 7449–7461. 1 indexed citations

8.

Lian, Xiangru & Ji Liu. (2019). Revisit Batch Normalization: New Understanding and Refinement via Composition Optimization. International Conference on Artificial Intelligence and Statistics. 3254–3263. 14 indexed citations

9.

Liu, Ji, et al.. (2019). A Generic Active Learning Framework for Class Imbalance Applications.. British Machine Vision Conference. 121. 4 indexed citations

10.

Hu, Wenqing, et al.. (2019). Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent. Neural Information Processing Systems. 32. 6926–6935. 5 indexed citations

11.

Zhang, Hantian, Jerry Li, Kaan Kara, et al.. (2017). ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning. International Conference on Machine Learning. 70. 4035–4043. 52 indexed citations

12.

Lin, Youzuo, et al.. (2017). Cascaded Contextual Region-based Convolutional Neural Network for Event Detection from Time Series Signals: A Seismic Application.. arXiv (Cornell University). 2 indexed citations

13.

Lian, Xiangru, Ce Zhang, Huan Zhang, et al.. (2017). Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent. Neural Information Processing Systems. 30. 5330–5340. 188 indexed citations

14.

Zhang, Wei, Suyog Gupta, Xiangru Lian, & Ji Liu. (2016). Staleness-aware async-SGD for distributed deep learning. International Joint Conference on Artificial Intelligence. 2350–2356. 100 indexed citations

15.

Liu, Ji & Xiaojin Zhu. (2016). The teaching dimension of linear learners. arXiv (Cornell University). 17(1). 117–126. 3 indexed citations

16.

Kong, Deguang, Ryohei Fujimaki, Ji Liu, Feiping Nie, & Chris Ding. (2014). Exclusive Feature Learning on Arbitrary Structures via ell_1,2-norm. Neural Information Processing Systems. 27. 1655–1663. 45 indexed citations

17.

Mahesh, Sankaranarayana P., et al.. (2014). Ultrasound-enhanced ocular delivery of dexamethasone sodium phosphate: an in vivo study. Journal of Therapeutic Ultrasound. 2(1). 6–6. 27 indexed citations

18.

Wright, Stephen J., et al.. (2013). An Approximate, Efficient LP Solver for LP Rounding. Neural Information Processing Systems. 26. 2895–2903. 8 indexed citations

19.

Qi, Xin‐Rui, Jun Zhao, Ji Liu, et al.. (2013). Abnormal Retinoid and TrkB Signaling in the Prefrontal Cortex in Mood Disorders. Cerebral Cortex. 25(1). 75–83. 66 indexed citations

20.

Cong, Yang, Ji Liu, Junsong Yuan, & Jiebo Luo. (2013). Self-Supervised Online Metric Learning With Low Rank Constraint for Scene Categorization. IEEE Transactions on Image Processing. 22(8). 3179–3191. 45 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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