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.
Brain tumor segmentation with Deep Neural Networks
20162.1k citationsMohammad Havaei, Axel Davy et al.Medical Image Analysisprofile →
This map shows the geographic impact of Chris 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 Chris Pal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chris Pal more than expected).
This network shows the impact of papers produced by Chris 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 Chris Pal. The network helps show where Chris Pal may publish in the future.
Co-authorship network of co-authors of Chris Pal
This figure shows the co-authorship network connecting the top 25 collaborators of Chris Pal.
A scholar is included among the top collaborators of Chris 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 Chris Pal. Chris Pal is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Liu, Dayiheng, Jie Fu, Yidan Zhang, Chris Pal, & Jiancheng Lv. (2019). Revision in Continuous Space: Fine-Grained Control of Text Style Transfer.. arXiv (Cornell University).4 indexed citations
3.
Bengio, Yoshua, et al.. (2018). Probabilistic Planning with Sequential Monte Carlo methods. PolyPublie (École Polytechnique de Montréal).5 indexed citations
Serdyuk, Dmitriy, Nan Rosemary Ke, Alessandro Sordoni, et al.. (2018). Twin Networks: Matching the Future for Sequence Generation. PolyPublie (École Polytechnique de Montréal).16 indexed citations
Serdyuk, Dmitriy, Nan Rosemary Ke, Alessandro Sordoni, Chris Pal, & Yoshua Bengio. (2017). Twin Networks: Using the Future as a Regularizer.. arXiv (Cornell University).5 indexed citations
Vorontsov, Eugene, Chiheb Trabelsi, Samuel Kadoury, & Chris Pal. (2017). On orthogonality and learning recurrent networks with long term dependencies. International Conference on Machine Learning. 3570–3578.28 indexed citations
Havaei, Mohammad, Axel Davy, David Warde-Farley, et al.. (2016). Brain tumor segmentation with Deep Neural Networks. Medical Image Analysis. 35. 18–31.2102 indexed citations breakdown →
McCallum, Andrew, et al.. (2007). Improving author coreference by resource-bounded information gathering from the web. International Joint Conference on Artificial Intelligence. 429–434.55 indexed citations
14.
Baudisch, Patrick, Desney Tan, Drew Steedly, et al.. (2007). AN EXPLORATION OF USER INTERFACE DESIGNS FOR REAL-TIME PANORAMIC PHOTOGRAPHY. AJIS. Australasian journal of information systems/AJIS. Australian journal of information systems/Australian journal of information systems.5 indexed citations
Pal, Chris & Andrew McCallum. (2006). CC Prediction with Graphical Models. Scholarworks (University of Massachusetts Amherst).17 indexed citations
17.
McCallum, Andrew, et al.. (2006). Multi-conditional learning: generative/discriminative training for clustering and classification. ScholarWorks@UMassAmherst (University of Massachusetts Amherst). 433–439.66 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.