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.
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
This map shows the geographic impact of Alan Yuille'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 Alan Yuille with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alan Yuille more than expected).
This network shows the impact of papers produced by Alan Yuille. 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 Alan Yuille. The network helps show where Alan Yuille may publish in the future.
Co-authorship network of co-authors of Alan Yuille
This figure shows the co-authorship network connecting the top 25 collaborators of Alan Yuille.
A scholar is included among the top collaborators of Alan Yuille 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 Alan Yuille. Alan Yuille is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Mahmood, Faisal, Wenhao Xu, Nicholas J. Durr, Jeremiah Johnson, & Alan Yuille. (2019). Structured Prediction using cGANs with Fusion Discriminator. arXiv (Cornell University).
Xie, Lingxi & Alan Yuille. (2017). Genetic CNN.219 indexed citations
10.
Zhou, Yuyin, Lingxi Xie, Wei Shen, Elliot K. Fishman, & Alan Yuille. (2016). Pancreas Segmentation in Abdominal CT Scan: A Coarse-to-Fine Approach.. arXiv (Cornell University).16 indexed citations
11.
Wu, Shuang, Xuming He, Hongjing Lu, & Alan Yuille. (2010). A unified model of short-range and long-range motion perception. Neural Information Processing Systems. 23. 2478–2486.7 indexed citations
12.
Lu, Hongjing, et al.. (2009). Modeling the spacing effect in sequential category learning. Neural Information Processing Systems. 22. 1159–1167.
13.
Lu, Hongjing, Randall R. Rojas, Tom Beckers, & Alan Yuille. (2008). Sequential Causal Learning in Humans and Rats. eScholarship (California Digital Library). 30(30). 185–190.16 indexed citations
Lu, Hongjing & Alan Yuille. (2007). The Noisy-Logical Distribution and its Application to Causal Inference. Neural Information Processing Systems. 20. 1673–1680.16 indexed citations
16.
Lee, Tai Sing & Alan Yuille. (2006). Efficient Coding of Visual Scenes by Grouping and Segmentation: Theoretical Predictions and Biological Evidence. eScholarship (California Digital Library).1 indexed citations
17.
Yuille, Alan. (2004). The Convergence of Contrastive Divergences. eScholarship (California Digital Library). 17. 1593–1600.11 indexed citations
18.
Rangarajan, Anand & Alan Yuille. (2001). MIME: Mutual Information Minimization and Entropy Maximization for Bayesian Belief Propagation. Neural Information Processing Systems. 14. 873–880.1 indexed citations
19.
Yuille, Alan & Shimon Ullman. (1990). Computational theories of low-level vision. MIT Press eBooks. 5–39.13 indexed citations
20.
Yuille, Alan, et al.. (1990). A coordinated approach to interviewing in child sexual abuse investigations..9 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.