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 Ordinal Regression Network for Monocular Depth Estimation
20181.1k citationsHuan Fu, Mingming Gong et al.profile →
CRIS: CLIP-Driven Referring Image Segmentation
2022194 citationsMingming Gong, Tongliang Liu et al.profile →
Machine learning (ML)-centric resource management in cloud computing: A review and future directions
This map shows the geographic impact of Mingming Gong'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 Mingming Gong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mingming Gong more than expected).
This network shows the impact of papers produced by Mingming Gong. 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 Mingming Gong. The network helps show where Mingming Gong may publish in the future.
Co-authorship network of co-authors of Mingming Gong
This figure shows the co-authorship network connecting the top 25 collaborators of Mingming Gong.
A scholar is included among the top collaborators of Mingming Gong 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 Mingming Gong. Mingming Gong is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Liu, Youfa, Bo Du, Weiping Tu, et al.. (2020). LogDet Metric-Based Domain Adaptation. IEEE Transactions on Neural Networks and Learning Systems. 31(11). 4673–4687.7 indexed citations
13.
Yao, Yu, Tongliang Liu, Bo Han, et al.. (2020). Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. Neural Information Processing Systems. 33. 7260–7271.5 indexed citations
14.
Guo, Jiaxian, Mingming Gong, Tongliang Liu, Kun Zhang, & Dacheng Tao. (2020). LTF: A Label Transformation Framework for Correcting Label Shift. International Conference on Machine Learning. 1. 3843–3853.8 indexed citations
15.
Zhang, Kun, Mingming Gong, Petar Stojanov, et al.. (2020). Domain Adaptation as a Problem of Inference on Graphical Models. Neural Information Processing Systems. 33. 4965–4976.1 indexed citations
Huang, Biwei, Kun Zhang, Pengtao Xie, et al.. (2019). Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering. Minerva Access (University of Melbourne). 32. 13510–13521.5 indexed citations
19.
Zhang, Kun, Mingming Gong, Joseph Ramsey, et al.. (2018). Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results.. Minerva Access (University of Melbourne). 1063–1072.5 indexed citations
20.
Gong, Mingming, Kun Zhang, Bernhard Schoelkopf, Dacheng Tao, & Philipp Geiger. (2015). Discovering Temporal Causal Relations from Subsampled Data. MPG.PuRe (Max Planck Society). 1898–1906.23 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.