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.
How transparency modulates trust in artificial intelligence
202291 citationsUmang Bhatt, Adrian Weller et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Adrian Weller'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 Adrian Weller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adrian Weller more than expected).
This network shows the impact of papers produced by Adrian Weller. 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 Adrian Weller. The network helps show where Adrian Weller may publish in the future.
Co-authorship network of co-authors of Adrian Weller
This figure shows the co-authorship network connecting the top 25 collaborators of Adrian Weller.
A scholar is included among the top collaborators of Adrian Weller 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 Adrian Weller. Adrian Weller is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Dickerson, John P., et al.. (2021). Exploring Alignment of Representations with Human Perception. arXiv (Cornell University).1 indexed citations
8.
Davis, Jared Quincy, et al.. (2020). CWY Parametrization for Scalable Learning of Orthogonal and Stiefel Matrices. arXiv (Cornell University).1 indexed citations
9.
Bakker, Michiel A., et al.. (2020). Fair Enough: Improving Fairness in Budget-Constrained Decision Making Using Confidence Thresholds.. DSpace@MIT (Massachusetts Institute of Technology). 41–53.5 indexed citations
10.
Choromański, Krzysztof, et al.. (2019). Unifying Orthogonal Monte Carlo Methods. International Conference on Machine Learning. 1203–1212.4 indexed citations
11.
Choromański, Krzysztof, Mark Rowland, Tamás Sarlós, et al.. (2018). The Geometry of Random Features. International Conference on Artificial Intelligence and Statistics. 1–9.3 indexed citations
12.
Adel, Tameem, Zoubin Ghahramani, & Adrian Weller. (2018). Discovering Interpretable Representations for Both Deep Generative and Discriminative Models. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 50–59.20 indexed citations
Rowland, Mark & Adrian Weller. (2017). Uprooting and Rerooting Higher-Order Graphical Models. Neural Information Processing Systems. 30. 209–218.1 indexed citations
15.
Choromański, Krzysztof, Mark Rowland, & Adrian Weller. (2017). The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings. Cambridge University Engineering Department Publications Database. 30. 219–228.7 indexed citations
16.
Rowland, Mark, Aldo Pacchiano, & Adrian Weller. (2017). Conditions beyond treewidth for tightness of higher-order LP relaxations. International Conference on Artificial Intelligence and Statistics. 10–18.1 indexed citations
17.
Weller, Adrian. (2015). Bethe and related pairwise entropy approximations. Uncertainty in Artificial Intelligence. 942–951.6 indexed citations
18.
Weller, Adrian. (2015). Revisiting the Limits of MAP Inference by MWSS on Perfect Graphs. Apollo (University of Cambridge). 1061–1069.5 indexed citations
19.
Weller, Adrian, et al.. (2014). Understanding the Bethe approximation: when and how can it go wrong?. Uncertainty in Artificial Intelligence. 868–877.10 indexed citations
20.
Weller, Adrian & Tony Jebara. (2014). Clamping Variables and Approximate Inference. Neural Information Processing Systems. 27. 909–917.8 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.