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.
Going deeper with convolutions
201530.6k citationsChristian Szegedy, Wei Liu et al.profile →
Learning Deep Representations of Fine-Grained Visual Descriptions
2016480 citationsScott Reed, Zeynep Akata et al.profile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
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This map shows the geographic impact of Scott Reed'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 Scott Reed with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scott Reed more than expected).
This network shows the impact of papers produced by Scott Reed. 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 Scott Reed. The network helps show where Scott Reed may publish in the future.
Co-authorship network of co-authors of Scott Reed
This figure shows the co-authorship network connecting the top 25 collaborators of Scott Reed.
A scholar is included among the top collaborators of Scott Reed 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 Scott Reed. Scott Reed 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.
Wang, Ziyu, Alexander Novikov, Konrad Żołna, et al.. (2020). Critic Regularized Regression. arXiv (Cornell University). 33. 7768–7778.1 indexed citations
2.
Cabi, Serkan, Sergio Gómez Colmenarejo, Alexander Novikov, et al.. (2019). A Framework for Data-Driven Robotics. arXiv (Cornell University).4 indexed citations
3.
Pierrot, Thomas, et al.. (2019). Learning Compositional Neural Programs with Recursive Tree Search and Planning. HAL (Le Centre pour la Communication Scientifique Directe).2 indexed citations
Reed, Scott, Yusuf Aytar, Ziyu Wang, et al.. (2018). Visual Imitation with a Minimal Adversary.1 indexed citations
6.
Trask, Andrew, Felix Hill, Scott Reed, et al.. (2018). Neural Arithmetic Logic Units. arXiv (Cornell University). 31. 8046–8055.27 indexed citations
7.
Wang, Ziyu, Josh Merel, Scott Reed, et al.. (2017). Robust imitation of diverse behaviors. Neural Information Processing Systems. 30. 5326–5335.25 indexed citations
8.
Reed, Scott, et al.. (2017). Coupled control of a lightweight ROV and manipulator arm for intervention tasks.3 indexed citations
9.
Reed, Scott & Nando de Freitas. (2016). Neural Programmer-Interpreters. arXiv (Cornell University).89 indexed citations
Reed, Scott, Honglak Lee, Dragomir Anguelov, et al.. (2015). TRAINING DEEP NEURAL NETWORKS ON NOISY LABELS WITH BOOTSTRAPPING. arXiv (Cornell University).145 indexed citations
13.
Reed, Scott, Yi Zhang, Yuting Zhang, & Honglak Lee. (2015). Deep visual analogy-making. Neural Information Processing Systems. 28. 1252–1260.71 indexed citations
14.
Szegedy, Christian, Wei Liu, Yangqing Jia, et al.. (2015). Going deeper with convolutions. 1–9.30609 indexed citations breakdown →
15.
Reed, Scott, Kihyuk Sohn, Yuting Zhang, & Honglak Lee. (2014). Learning to Disentangle Factors of Variation with Manifold Interaction. International Conference on Machine Learning. 1431–1439.86 indexed citations
Lesser, Victor, et al.. (1980). Quantifying and simulating the behavior of knowledge-based interpretation systems. National Conference on Artificial Intelligence. 111–115.12 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.