Yelong Shen

6.8k total citations · 2 hit papers
50 papers, 1.8k citations indexed

About

Yelong Shen is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Yelong Shen has authored 50 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Artificial Intelligence, 21 papers in Computer Vision and Pattern Recognition and 5 papers in Information Systems. Recurrent topics in Yelong Shen's work include Topic Modeling (30 papers), Natural Language Processing Techniques (19 papers) and Multimodal Machine Learning Applications (13 papers). Yelong Shen is often cited by papers focused on Topic Modeling (30 papers), Natural Language Processing Techniques (19 papers) and Multimodal Machine Learning Applications (13 papers). Yelong Shen collaborates with scholars based in United States, China and United Kingdom. Yelong Shen's co-authors include Jianfeng Gao, Xiaodong He, Li Deng, Grégoire Mesnil, Weizhu Chen, Ruoming Jin, Xiaodong Liu, Po-Sen Huang, Kevin Duh and Xiaodong Liu and has published in prestigious journals such as Knowledge and Information Systems, National University of Singapore and arXiv (Cornell University).

In The Last Decade

Yelong Shen

48 papers receiving 1.7k citations

Hit Papers

align trajectories sort by overperformance

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.

Learning semantic representations using convolutional neural networks for web search

2014 419 citations Yelong Shen, Xiaodong He et al. profile →
A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval

2014 401 citations Yelong Shen, Xiaodong He et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Yelong Shen United States	17	1.4k	532	477	96	89	50	1.8k
Shujian Huang China	21	1.5k 1.1×	539 1.0×	621 1.3×	111 1.2×	46 0.5×	96	1.8k
Xiting Wang China	18	802 0.6×	480 0.9×	379 0.8×	86 0.9×	69 0.8×	33	1.2k
Aliaksei Severyn Italy	18	1.7k 1.2×	289 0.5×	509 1.1×	64 0.7×	80 0.9×	30	1.9k
Minghui Qiu China	23	1.5k 1.0×	501 0.9×	676 1.4×	78 0.8×	45 0.5×	85	2.1k
Mark Carman Australia	19	1.1k 0.7×	251 0.5×	537 1.1×	84 0.9×	119 1.3×	79	1.6k
Zheng Chen China	20	1.8k 1.3×	287 0.5×	798 1.7×	78 0.8×	208 2.3×	63	2.2k
Yinwei Wei China	18	994 0.7×	793 1.5×	994 2.1×	75 0.8×	87 1.0×	56	1.7k
Dou Shen China	19	1.1k 0.8×	319 0.6×	760 1.6×	54 0.6×	188 2.1×	50	1.7k
Yansong Feng China	25	1.8k 1.2×	564 1.1×	494 1.0×	169 1.8×	258 2.9×	102	2.4k

Countries citing papers authored by Yelong Shen

Since Specialization

Citations

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

Fields of papers citing papers by Yelong Shen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yelong Shen

This figure shows the co-authorship network connecting the top 25 collaborators of Yelong Shen. A scholar is included among the top collaborators of Yelong Shen 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 Yelong Shen. Yelong Shen 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

Wang, Yiping, et al.. (2025). Is Your World Simulator a Good Story Presenter? A Consecutive Events-Based Benchmark for Future Long Video Generation. 13629–13638.

Yang, Yaming, Yelong Shen, Yujing Wang, et al.. (2025). MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 39(20). 22010–22018. 3 indexed citations

Luo, Yi, Yuhao Zhang, Chengjin Xu, et al.. (2024). Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models. 1 indexed citations

Shao, Zhihong, Yeyun Gong, Yelong Shen, et al.. (2023). Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy. 9248–9274. 58 indexed citations

Cheng, Yu, et al.. (2023). In-Context Learning Unlocked for Diffusion Models. 8542–8562.

Li, Xiaonan, Daya Guo, Yeyun Gong, et al.. (2022). Soft-Labeled Contrastive Pre-Training for Function-Level Code Representation. 118–129. 5 indexed citations

He, Di, Yelong Shen, Tie‐Yan Liu, et al.. (2022). Finding the Dominant Winning Ticket in Pre-Trained Language Models. Findings of the Association for Computational Linguistics: ACL 2022. 1459–1472. 4 indexed citations

Zhou, Kun, Yeyun Gong, Xiao Liu, et al.. (2022). SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval. 548–559. 11 indexed citations

Jin, Woojeong, Yu Cheng, Yelong Shen, Weizhu Chen, & Xiang Ren. (2022). A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2763–2775. 54 indexed citations

10.

Li, Xiaonan, Yeyun Gong, Yelong Shen, et al.. (2022). CodeRetriever: A Large Scale Contrastive Pre-Training Method for Code Search. 2898–2910. 11 indexed citations

11.

Mao, Yuning, Pengcheng He, Xiaodong Liu, et al.. (2021). Generation-Augmented Retrieval for Open-Domain Question Answering. 4089–4100. 98 indexed citations

12.

Cheng, Hao, Yelong Shen, Xiaodong Liu, et al.. (2021). UnitedQA: A Hybrid Approach for Open Domain Question Answering. 3080–3090. 21 indexed citations

13.

Qu, Yanru, et al.. (2021). CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding. 12 indexed citations

14.

Zhang, Zijie, et al.. (2020). Adversarial Attacks on Deep Graph Matching. Neural Information Processing Systems. 33. 20834–20851. 13 indexed citations

15.

Xu, Yichong, Xiaodong Liu, Yelong Shen, Jingjing Liu, & Jianfeng Gao. (2018). Multi-Task Learning for Machine Reading Comprehension.. arXiv (Cornell University). 5 indexed citations

16.

Shen, Yelong, Jianshu Chen, Po-Sen Huang, Yuqing Guo, & Jianfeng Gao. (2018). M-Walk: Learning to Walk in Graph with Monte Carlo Tree Search. arXiv (Cornell University). 1 indexed citations

17.

Liu, Xiaodong, Yelong Shen, Kevin Duh, & Jianfeng Gao. (2018). Stochastic Answer Networks for Machine Reading Comprehension. 1694–1704. 96 indexed citations

18.

Shen, Yelong, Po-Sen Huang, Ming‐Wei Chang, & Jianfeng Gao. (2017). Implicit ReasoNet: Modeling Large-Scale Structured Relationships with Shared Memory. arXiv (Cornell University). 5 indexed citations

19.

Huang, Hsin-Yuan, Chenguang Zhu, Yelong Shen, & Weizhu Chen. (2017). FusionNet: Fusing via Fully-aware Attention with Application to Machine Comprehension. International Conference on Learning Representations. 26 indexed citations

20.

Chen, Jianshu, Ji He, Yelong Shen, et al.. (2015). End-to-end learning of LDA by mirror-descent back propagation over a deep architecture. Neural Information Processing Systems. 28. 1765–1773. 11 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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