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.
Scaling distributed machine learning with the parameter server
2014606 citationsMu Li, David G. Andersen et al.Operating Systems Design and Implementationprofile →
Recurrent Recommender Networks
2017416 citationsChao-Yuan Wu, Amr Ahmed 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 Amr Ahmed'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 Amr Ahmed with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amr Ahmed more than expected).
This network shows the impact of papers produced by Amr Ahmed. 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 Amr Ahmed. The network helps show where Amr Ahmed may publish in the future.
Co-authorship network of co-authors of Amr Ahmed
This figure shows the co-authorship network connecting the top 25 collaborators of Amr Ahmed.
A scholar is included among the top collaborators of Amr Ahmed 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 Amr Ahmed. Amr Ahmed is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zaheer, Manzil, Amr Ahmed, & Alexander J. Smola. (2017). Latent LSTM Allocation: Joint clustering and non-linear dynamic modeling of sequence data. International Conference on Machine Learning. 3967–3976.23 indexed citations
10.
Zaheer, Manzil, Satwik Kottur, Amr Ahmed, José M. F. Moura, & Alexander J. Smola. (2017). Canopy --- Fast Sampling with Cover Trees. International Conference on Machine Learning. 3977–3986.2 indexed citations
11.
Wu, Chao-Yuan, Amr Ahmed, Alex Beutel, & Alexander J. Smola. (2017). Joint Training of Ratings and Reviews with Recurrent Recommender Networks. International Conference on Learning Representations.15 indexed citations
12.
Li, Mu, David G. Andersen, Jun Woo Park, et al.. (2014). Scaling distributed machine learning with the parameter server. Operating Systems Design and Implementation. 583–598.606 indexed citations breakdown →
13.
Ahmed, Amr, Liangjie Hong, & Alexander J. Smola. (2013). Nested Chinese Restaurant Franchise Process: Applications to User Tracking and Document Modeling. International Conference on Machine Learning. 1426–1434.21 indexed citations
14.
Ahmed, Amr, Sujith Ravi, Alex Smola, & Shravan Narayanamurthy. (2012). FastEx: Hash Clustering with Exponential Families. Neural Information Processing Systems. 25. 2798–2806.9 indexed citations
15.
Ahmed, Amr, Alexander J. Smola, & Markus Weimer. (2012). WWW 2012 Tutorial: New Templates for Scalable Data Analysis.2 indexed citations
Ahmed, Amr & Eric P. Xing. (2007). Seeking The Truly Correlated Topic Posterior - on tight approximate inference of logistic-normal admixture model. International Conference on Artificial Intelligence and Statistics. 19–26.11 indexed citations
19.
Wang, Ching‐Wei, Amr Ahmed, & Andrew Hunter. (2007). Locating the Upper Body of Covered Humans in application to Diagnosis of Obstructive Sleep Apnea. World Congress on Engineering. 2165(1). 662–667.8 indexed citations
20.
Wang, Ching‐Wei, Amr Ahmed, & Andrew Hunter. (2006). Artificial intelligent vision analysis in obstructive sleep apnoea (OSA). Lincoln Repository (University of Lincoln). 71(6). 22–22.1 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.