Damai Dai

2.8k total citations · 2 hit papers
18 papers, 530 citations indexed

About

Damai Dai is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Damai Dai has authored 18 papers receiving a total of 530 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 1 paper in Computer Networks and Communications. Recurrent topics in Damai Dai's work include Topic Modeling (11 papers), Natural Language Processing Techniques (5 papers) and Multimodal Machine Learning Applications (4 papers). Damai Dai is often cited by papers focused on Topic Modeling (11 papers), Natural Language Processing Techniques (5 papers) and Multimodal Machine Learning Applications (4 papers). Damai Dai collaborates with scholars based in China, India and United States. Damai Dai's co-authors include Zhifang Sui, Baobao Chang, Furu Wei, Yaru Hao, Shuming Ma, Qingxiu Dong, Jingjing Xu, Xu Sun, Zhiyong Wu and Heming Xia and has published in prestigious journals such as arXiv (Cornell University), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Damai Dai

17 papers receiving 514 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.

A Survey on In-context Learning

2024 173 citations Qingxiu Dong, Lei Li et al. profile →
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

2024 51 citations Damai Dai, Chengqi Deng et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Damai Dai China	10	359	125	67	27	26	18	530
Bing Yin United States	11	362 1.0×	102 0.8×	112 1.7×	35 1.3×	38 1.5×	36	528
Chih‐Kuan Yeh United States	7	348 1.0×	158 1.3×	53 0.8×	14 0.5×	33 1.3×	9	436
Yun-Cheng Wang United States	8	196 0.5×	62 0.5×	61 0.9×	24 0.9×	21 0.8×	23	347
Canwen Xu United States	9	691 1.9×	193 1.5×	73 1.1×	13 0.5×	21 0.8×	20	761
Mickaël Coustaty France	12	226 0.6×	262 2.1×	59 0.9×	12 0.4×	17 0.7×	55	514
Kaixuan Ji China	7	294 0.8×	97 0.8×	112 1.7×	61 2.3×	24 0.9×	15	461
Mounia Mikram Morocco	8	215 0.6×	76 0.6×	85 1.3×	17 0.6×	67 2.6×	35	403
Hengyi Cai China	9	269 0.7×	62 0.5×	66 1.0×	23 0.9×	10 0.4×	16	401
Zhiruo Wang United States	6	593 1.7×	182 1.5×	72 1.1×	18 0.7×	19 0.7×	11	670

Countries citing papers authored by Damai Dai

Since Specialization

Citations

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

Fields of papers citing papers by Damai Dai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Damai Dai

This figure shows the co-authorship network connecting the top 25 collaborators of Damai Dai. A scholar is included among the top collaborators of Damai Dai 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 Damai Dai. Damai Dai 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:

18 of 18 papers shown

1.

Zhao, Chenggang, Chengqi Deng, Chong Ruan, et al.. (2025). Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI Architectures. arXiv (Cornell University). 1731–1745. 5 indexed citations

2.

Yuan, Jingyang, Huazuo Gao, Damai Dai, et al.. (2025). Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention. 23078–23097. 3 indexed citations

3.

Chen, Deli, et al.. (2024). Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models. 784–801.

4.

Dai, Damai, Chengqi Deng, Chenggang Zhao, et al.. (2024). DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models. 1280–1297. 51 indexed citations breakdown →

5.

Dong, Qingxiu, Lei Li, Damai Dai, et al.. (2024). A Survey on In-context Learning. 1107–1128. 173 indexed citations breakdown →

6.

Dai, Damai, Yutao Sun, Dong Li, et al.. (2023). Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers. 4005–4019. 69 indexed citations

7.

Wang, Lean, Damai Dai, Deli Chen, et al.. (2023). Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning. 9840–9855. 10 indexed citations

8.

Dai, Damai, et al.. (2023). Denoising Bottleneck with Mutual Information Maximization for Video Multimodal Fusion. 2231–2243. 4 indexed citations

9.

Dong, Qingxiu, Damai Dai, Yifan Song, et al.. (2022). Calibrating Factual Knowledge in Pretrained Language Models. 5937–5947. 17 indexed citations

10.

Dai, Damai, Li Dong, Shuming Ma, et al.. (2022). StableMoE: Stable Routing Strategy for Mixture of Experts. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 14 indexed citations

11.

Dai, Damai, Li Dong, Yaru Hao, et al.. (2022). Knowledge Neurons in Pretrained Transformers. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 8493–8502. 68 indexed citations

12.

Zheng, Hua, Damai Dai, Lei Li, et al.. (2021). Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation. 5524–5531. 3 indexed citations

13.

Dai, Damai, Hua Zheng, Fuli Luo, et al.. (2021). Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions. 83–89. 6 indexed citations

14.

Zheng, Hua, Lei Li, Damai Dai, et al.. (2021). Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation. 918–923. 3 indexed citations

15.

Wang, Peiyi, et al.. (2021). Behind the Scenes. 1969–1978. 7 indexed citations

16.

Luo, Fuli, Damai Dai, Pengcheng Yang, et al.. (2019). Learning to Control the Fine-grained Sentiment for Story Ending Generation. 6020–6026. 29 indexed citations

17.

Sui, Zhifang, et al.. (2019). Preliminary Study on the Construction of Chinese Medical Knowledge Graph. 33(10). 1–9. 36 indexed citations

18.

Ma, Shuming, Lei Cui, Damai Dai, Furu Wei, & Xu Sun. (2019). LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 6810–6817. 32 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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