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.
Caffe
20147.8k citationsYangqing Jia, Jeff Donahue et al.profile →
Long-term recurrent convolutional networks for visual recognition and description
20153.1k citationsJeff Donahue, Lisa Anne Hendricks et al.profile →
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description
20161.1k citationsJeff Donahue, Lisa Anne Hendricks et al.IEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
Im2Calories: Towards an Automated Mobile Vision Food Diary
2015320 citationsAnoop Korattikara, Sergio Guadarrama et al.profile →
YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-Shot Recognition
2013319 citationsSergio Guadarrama, Subhashini Venugopalan et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Sergio Guadarrama
Since
Specialization
Citations
This map shows the geographic impact of Sergio Guadarrama'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 Sergio Guadarrama with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sergio Guadarrama more than expected).
Fields of papers citing papers by Sergio Guadarrama
This network shows the impact of papers produced by Sergio Guadarrama. 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 Sergio Guadarrama. The network helps show where Sergio Guadarrama may publish in the future.
Co-authorship network of co-authors of Sergio Guadarrama
This figure shows the co-authorship network connecting the top 25 collaborators of Sergio Guadarrama.
A scholar is included among the top collaborators of Sergio Guadarrama 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 Sergio Guadarrama. Sergio Guadarrama is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Chan, Stephanie C. Y., et al.. (2020). Measuring the Reliability of Reinforcement Learning Algorithms. arXiv (Cornell University).6 indexed citations
2.
Liu, Siqi, Zhenhai Zhu, Ning Ye, Sergio Guadarrama, & Kevin D. Murphy. (2016). Optimization of image description metrics using policy gradient methods. arXiv (Cornell University).31 indexed citations
3.
Donahue, Jeff, Lisa Anne Hendricks, Marcus Rohrbach, et al.. (2016). Long-Term Recurrent Convolutional Networks for Visual Recognition and Description. IEEE Transactions on Pattern Analysis and Machine Intelligence. 39(4). 677–691.1096 indexed citations breakdown →
4.
Hoffman, Judy, Deepak Pathak, Eric Tzeng, et al.. (2016). Large scale visual recognition through adaptation using joint representation and multiple instance learning. 17(1). 4954–4984.8 indexed citations
5.
Donahue, Jeff, Lisa Anne Hendricks, Sergio Guadarrama, et al.. (2015). Long-term recurrent convolutional networks for visual recognition and description. 2625–2634.3070 indexed citations breakdown →
6.
Thomason, Jesse, Subhashini Venugopalan, Sergio Guadarrama, Kate Saenko, & Raymond J. Mooney. (2014). Integrating Language and Vision to Generate Natural Language Descriptions of Videos in the Wild. International Conference on Computational Linguistics. 1218–1227.107 indexed citations
Ibáñez, Óscar, Óscar Cordón, Sergio Damas, Sergio Guadarrama, & José Santamaría. (2009). A new approach to fuzzy location of cephalometric landmarks in craniofacial superimposition. European Society for Fuzzy Logic and Technology Conference. 195–200.5 indexed citations
13.
Guadarrama, Sergio. (2009). Computing with Actions: The case of driving a car in a simulated car race. European Society for Fuzzy Logic and Technology Conference. 1410–1415.2 indexed citations
Herrera‐Viedma, Enrique, et al.. (2005). Soft Computing for Information Retrieval in the WEB.. European Society for Fuzzy Logic and Technology Conference. 4–6.3 indexed citations
17.
Guadarrama, Sergio, et al.. (2005). A Reflection on the use of And.. European Society for Fuzzy Logic and Technology Conference. 571–575.
Muñoz-Hernández, Susana, et al.. (2002). Combining Crisp and Fuzzy Logic in a Prolog Compiler.. 23–38.4 indexed citations
20.
Guadarrama, Sergio, et al.. (2002). Fuzzy Prolog: A Simple General Implementation Using CLP(R). International Conference on Logic Programming. 450–464.14 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.