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.
Removing Rain from Single Images via a Deep Detail Network
2017796 citationsXueyang Fu, Jia‐Bin Huang et al.profile →
Stochastic variational inference
2013669 citationsMatthew D. Hoffman, David M. Blei et al.Journal of Machine Learning Researchprofile →
Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal
2017632 citationsXueyang Fu, Jia‐Bin Huang et al.IEEE Transactions on Image Processingprofile →
PanNet: A Deep Network Architecture for Pan-Sharpening
2017577 citationsXueyang Fu, Yue Huang et al.profile →
A fusion-based enhancing method for weakly illuminated images
2016547 citationsXueyang Fu, Delu Zeng et al.profile →
Lightweight Pyramid Networks for Image Deraining
2019321 citationsXueyang Fu, Yue Huang et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of John Paisley'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 John Paisley with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Paisley more than expected).
This network shows the impact of papers produced by John Paisley. 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 John Paisley. The network helps show where John Paisley may publish in the future.
Co-authorship network of co-authors of John Paisley
This figure shows the co-authorship network connecting the top 25 collaborators of John Paisley.
A scholar is included among the top collaborators of John Paisley 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 John Paisley. John Paisley is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhang, Wei, et al.. (2024). Gaussian Process Neural Additive Models. Proceedings of the AAAI Conference on Artificial Intelligence. 38(15). 16865–16872.2 indexed citations
Tu, Tao, John Paisley, Stefan Haufe, & Paul Sajda. (2019). A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI. Neural Information Processing Systems. 32. 4662–4671.4 indexed citations
Paisley, John, et al.. (2018). CRVI: Convex Relaxation for Variational Inference. International Conference on Machine Learning. 1477–1485.3 indexed citations
9.
Zhang, Aonan & John Paisley. (2018). Deep Bayesian Nonparametric Tracking.. International Conference on Machine Learning. 5828–5836.5 indexed citations
10.
Fu, Xueyang, Jia‐Bin Huang, Delu Zeng, et al.. (2017). Removing Rain from Single Images via a Deep Detail Network. 1715–1723.796 indexed citations breakdown →
11.
Paisley, John, et al.. (2016). Markov latent feature models. International Conference on Machine Learning. 1129–1137.1 indexed citations
12.
Zhang, Aonan, et al.. (2016). Stochastic Variational Inference for the HDP-HMM.. International Conference on Artificial Intelligence and Statistics. 800–808.5 indexed citations
13.
Zhang, Aonan & John Paisley. (2015). Markov Mixed Membership Models. International Conference on Machine Learning. 475–483.5 indexed citations
14.
Liang, Dawen & John Paisley. (2015). Landmarking Manifolds with Gaussian Processes. International Conference on Machine Learning. 466–474.4 indexed citations
15.
Hoffman, Matthew D., David M. Blei, Chong Wang, & John Paisley. (2013). Stochastic variational inference. Journal of Machine Learning Research. 14(1). 1303–1347.669 indexed citations breakdown →
16.
Paisley, John, David M. Blei, & Michael I. Jordan. (2012). Stick-Breaking Beta Processes and the Poisson Process. International Conference on Artificial Intelligence and Statistics. 850–858.13 indexed citations
17.
Wang, Chong, John Paisley, & David M. Blei. (2011). Online Variational Inference for the Hierarchical Dirichlet Process. Journal of Machine Learning Research. 15. 752–760.194 indexed citations
18.
Zhou, Mingyuan, et al.. (2009). Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations. Neural Information Processing Systems. 22. 2295–2303.147 indexed citations
Echeverria, P, George R. Siber, John Paisley, et al.. (1975). Age-dependent dose response to gentamicin. The Journal of Pediatrics. 87(5). 805–808.22 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.