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.
Deep Learning for Extreme Multi-label Text Classification
2017388 citationsWei-Cheng Chang, Yuexin Wu et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Wei-Cheng Chang
Since
Specialization
Citations
This map shows the geographic impact of Wei-Cheng Chang'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 Wei-Cheng Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wei-Cheng Chang more than expected).
This network shows the impact of papers produced by Wei-Cheng Chang. 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 Wei-Cheng Chang. The network helps show where Wei-Cheng Chang may publish in the future.
Co-authorship network of co-authors of Wei-Cheng Chang
This figure shows the co-authorship network connecting the top 25 collaborators of Wei-Cheng Chang.
A scholar is included among the top collaborators of Wei-Cheng Chang 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 Wei-Cheng Chang. Wei-Cheng Chang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Xiong, Yuanhao, Wei-Cheng Chang, Cho‐Jui Hsieh, Hsiang‐Fu Yu, & Inderjit S. Dhillon. (2022). Extreme Zero-Shot Learning for Extreme Text Classification. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 5455–5468.7 indexed citations
6.
Jiang, Jyun‐Yu, Wei-Cheng Chang, Jiong Zhang, Cho‐Jui Hsieh, & Hsiang‐Fu Yu. (2022). Relevance under the Iceberg. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1870–1874.3 indexed citations
7.
Yu, Hsiang‐Fu, Jiong Zhang, Wei-Cheng Chang, et al.. (2022). PECOS. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4848–4849.10 indexed citations
Chang, Wei-Cheng, Hsiang‐Fu Yu, Kai Zhong, Yiming Yang, & Inderjit S. Dhillon. (2019). X-BERT: eXtreme Multi-label Text Classification with BERT. arXiv (Cornell University).5 indexed citations
12.
Chang, Wei-Cheng, Hsiang‐Fu Yu, Kai Zhong, Yiming Yang, & Inderjit S. Dhillon. (2019). A Modular Deep Learning Approach for Extreme Multi-label Text Classification.. arXiv (Cornell University).7 indexed citations
13.
Chang, Wei-Cheng, Hsiang‐Fu Yu, Kai Zhong, Yiming Yang, & Inderjit S. Dhillon. (2019). X-BERT: eXtreme Multi-label Text Classification with using Bidirectional Encoder Representations from Transformers.14 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.