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.
Deep Learning: A Primer for Radiologists
2017788 citationsEugene Vorontsov, Simon Turcotte et al.profile →
Describing Videos by Exploiting Temporal Structure
2015593 citationsLi Yao, Atousa Torabi et al.PolyPublie (École Polytechnique de Montréal)profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Christopher Pal
Since
Specialization
Citations
This map shows the geographic impact of Christopher Pal'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 Christopher Pal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christopher Pal more than expected).
This network shows the impact of papers produced by Christopher Pal. 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 Christopher Pal. The network helps show where Christopher Pal may publish in the future.
Co-authorship network of co-authors of Christopher Pal
This figure shows the co-authorship network connecting the top 25 collaborators of Christopher Pal.
A scholar is included among the top collaborators of Christopher Pal 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 Christopher Pal. Christopher Pal is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Beckham, Christopher, Sina Honari, Alex Lamb, et al.. (2019). Adversarial Mixup Resynthesizers. PolyPublie (École Polytechnique de Montréal).9 indexed citations
Ke, Nan Rosemary, Konrad Żołna, Alessandro Sordoni, et al.. (2018). Focused Hierarchical RNNs for Conditional Sequence Processing. PolyPublie (École Polytechnique de Montréal). 2554–2563.9 indexed citations
13.
Racah, Evan, Christopher Beckham, Tegan Maharaj, Prabhat, & Christopher Pal. (2017). Semi-Supervised Detection of Extreme Weather Events in Large Climate Datasets. arXiv (Cornell University).7 indexed citations
14.
Mudigonda, Mayur, Karthik Kashinath, Christopher Beckham, et al.. (2017). Deep Learning for Extreme Weather Detection. AGU Fall Meeting Abstracts. 2017.3 indexed citations
15.
Witten, Ian H., Eibe Frank, Mark A. Hall, & Christopher Pal. (2016). Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers Inc. eBooks.132 indexed citations
16.
Yao, Li, Atousa Torabi, Kyunghyun Cho, et al.. (2015). Describing Videos by Exploiting Temporal Structure. PolyPublie (École Polytechnique de Montréal). 4507–4515.593 indexed citations breakdown →
17.
Pal, Christopher, et al.. (2012). Automated person segmentation in videos. PolyPublie (École Polytechnique de Montréal).2 indexed citations
18.
Wei, Bin & Christopher Pal. (2010). Cross Lingual Adaptation: An Experiment on Sentiment Classifications. Meeting of the Association for Computational Linguistics. 258–262.52 indexed citations
19.
Pal, Christopher, et al.. (2009). Semi-supervised learning of visual classifiers from web images and text. International Joint Conference on Artificial Intelligence. 1169–1174.6 indexed citations
20.
Messing, Ross & Christopher Pal. (2009). Behavior Recognition in Video with Extended Models of Feature Velocity Dynamics.. PolyPublie (École Polytechnique de Montréal). 56–61.1 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.