Countries citing papers authored by Pascal Poupart
Since
Specialization
Citations
This map shows the geographic impact of Pascal Poupart'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 Pascal Poupart with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pascal Poupart more than expected).
This network shows the impact of papers produced by Pascal Poupart. 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 Pascal Poupart. The network helps show where Pascal Poupart may publish in the future.
Co-authorship network of co-authors of Pascal Poupart
This figure shows the co-authorship network connecting the top 25 collaborators of Pascal Poupart.
A scholar is included among the top collaborators of Pascal Poupart 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 Pascal Poupart. Pascal Poupart is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kazemi, Seyed Mehran, et al.. (2019). Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey.. arXiv (Cornell University).10 indexed citations
4.
Jaini, Priyank, Pascal Poupart, & Yaoliang Yu. (2018). Deep Homogeneous Mixture Models: Representation, Separation, and Approximation. Neural Information Processing Systems. 31. 7136–7145.3 indexed citations
5.
Jaini, Priyank, Zhitang Chen, Edith Law, et al.. (2017). Online Bayesian Transfer Learning for Sequential Data Modeling. International Conference on Learning Representations.11 indexed citations
6.
Zhao, Han, Pascal Poupart, & Geoffrey J. Gordon. (2016). A Unified Approach for Learning the Parameters of Sum-Product Networks. Neural Information Processing Systems. 29. 433–441.1 indexed citations
7.
Zhao, Han, et al.. (2016). Online and distributed Bayesian moment matching for parameter learning in sum-product networks. International Conference on Artificial Intelligence and Statistics. 1469–1477.12 indexed citations
8.
Poupart, Pascal, et al.. (2016). Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics. Neural Information Processing Systems. 29. 4529–4537.6 indexed citations
9.
Jaini, Priyank, et al.. (2016). Online Algorithms for Sum-Product Networks with Continuous Variables. 52(2016). 228–239.6 indexed citations
10.
Grześ, Marek, Pascal Poupart, & Jesse Hoey. (2013). Isomorph-free branch and bound search for finite state controllers. Kent Academic Repository (University of Kent). 2282–2290.11 indexed citations
11.
Sanner, Scott, et al.. (2012). Symbolic Dynamic Programming for Continuous State and Observation POMDPs. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 25. 1394–1402.6 indexed citations
Tung, James, et al.. (2011). Ambulatory Assessment of Lifestyle Factors for Alzheimer's Disease and Related Dementias. National Conference on Artificial Intelligence.2 indexed citations
15.
Poupart, Pascal, et al.. (2008). Explaining recommendations generated by MDPs. 13–24.5 indexed citations
16.
Poupart, Pascal & Nikos Vlassis. (2008). Model-based Bayesian reinforcement learning in partially observable domains. Open Repository and Bibliography (University of Luxembourg).35 indexed citations
17.
Porta, Josep M., Nikos Vlassis, Matthijs T. J. Spaan, & Pascal Poupart. (2006). Point-Based Value Iteration for Continuous POMDPs. Journal of Machine Learning Research. 7(83). 2329–2367.160 indexed citations
18.
Regan, Kevin, Robin Cohen, & Pascal Poupart. (2005). The Advisor-POMDP: A Principled Approach to Trust through Reputation in Electronic Markets..16 indexed citations
19.
Hoey, Jesse & Pascal Poupart. (2005). Solving POMDPs with continuous or large discrete observation spaces. Discovery Research Portal (University of Dundee). 1332–1338.67 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.