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.
Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions
2020451 citationsHan-Jia Ye, Hexiang Hu et al.profile →
This map shows the geographic impact of De‐Chuan Zhan'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 De‐Chuan Zhan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites De‐Chuan Zhan more than expected).
This network shows the impact of papers produced by De‐Chuan Zhan. 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 De‐Chuan Zhan. The network helps show where De‐Chuan Zhan may publish in the future.
Co-authorship network of co-authors of De‐Chuan Zhan
This figure shows the co-authorship network connecting the top 25 collaborators of De‐Chuan Zhan.
A scholar is included among the top collaborators of De‐Chuan Zhan 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 De‐Chuan Zhan. De‐Chuan Zhan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ye, Han-Jia, et al.. (2024). The Capacity and Robustness Trade-Off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting. IEEE Transactions on Knowledge and Data Engineering. 36(11). 7129–7142.63 indexed citations breakdown →
Ye, Han-Jia, et al.. (2022). Few-Shot Learning With a Strong Teacher. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(3). 1425–1440.39 indexed citations
Ye, Han-Jia, et al.. (2016). Learning Feature Aware Metric. Asian Conference on Machine Learning. 286–301.2 indexed citations
18.
Yang, Yang, Han-Jia Ye, De‐Chuan Zhan, & Yuan Jiang. (2015). Auxiliary information regularized machine for multiple modality feature learning. International Conference on Artificial Intelligence. 1033–1039.11 indexed citations
19.
Nguyen, Cam-Tu, De‐Chuan Zhan, & Zhi‐Hua Zhou. (2013). Multi-modal image annotation with multi-instance multi-label LDA. International Joint Conference on Artificial Intelligence. 1558–1564.47 indexed citations
20.
Zhou, Zhi‐Hua, De‐Chuan Zhan, & Qiang Yang. (2007). Semi-supervised learning with very few labeled training examples. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1. 675–680.91 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.