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.
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
2022832 citationsChengyi Wang, Yu Wu et al.profile →
An Empirical Study of Training End-to-End Vision-and-Language Transformers
2022204 citationsYichong Xu, Shuohang Wang et al.profile →
Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks
202454 citationsBin Xiao, Haiping Wu et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Michael Zeng'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 Michael Zeng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Zeng more than expected).
This network shows the impact of papers produced by Michael Zeng. 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 Michael Zeng. The network helps show where Michael Zeng may publish in the future.
Co-authorship network of co-authors of Michael Zeng
This figure shows the co-authorship network connecting the top 25 collaborators of Michael Zeng.
A scholar is included among the top collaborators of Michael Zeng 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 Michael Zeng. Michael Zeng is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Xiao, Bin, Haiping Wu, Weijian Xu, et al.. (2024). Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks. 4818–4829.54 indexed citations breakdown →
Liu, Yang, Chenguang Zhu, & Michael Zeng. (2022). End-to-End Segmentation-based News Summarization. Findings of the Association for Computational Linguistics: ACL 2022. 544–554.10 indexed citations
Wang, Chengyi, Yu Wu, Yao Qian, et al.. (2021). UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data. International Conference on Machine Learning. 10937–10947.14 indexed citations
Zhu, Chenguang, Ruochen Xu, Michael Zeng, & Xuedong Huang. (2020). End-to-End Abstractive Summarization for Meetings.. arXiv (Cornell University).7 indexed citations
15.
Zhu, Chenguang, Ruochen Xu, Qingkai Zeng, et al.. (2020). Boosting Factual Correctness of Abstractive Summarization with Knowledge Graph.. arXiv (Cornell University).23 indexed citations
16.
Peng, Baolin, Chenguang Zhu, Michael Zeng, & Jianfeng Gao. (2020). Data Augmentation for Spoken Language Understanding via Pretrained Models. arXiv (Cornell University).11 indexed citations
17.
Zhu, Chenguang, Ruochen Xu, Qingkai Zeng, et al.. (2020). Boosting Factual Correctness of Abstractive Summarization. arXiv (Cornell University).4 indexed citations
Zhu, Chenguang, et al.. (2019). Make Lead Bias in Your Favor: A Simple and Effective Method for News Summarization. arXiv (Cornell University).8 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.