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.
The “Something Something” Video Database for Learning and Evaluating Visual Common Sense
2017772 citationsRaghav Goyal, Samira Ebrahimi Kahou et al.profile →
Citations per year, relative to Vincent Michalski Vincent Michalski (= 1×)
peers
Samira Ebrahimi Kahou
Countries citing papers authored by Vincent Michalski
Since
Specialization
Citations
This map shows the geographic impact of Vincent Michalski'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 Vincent Michalski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vincent Michalski more than expected).
Fields of papers citing papers by Vincent Michalski
This network shows the impact of papers produced by Vincent Michalski. 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 Vincent Michalski. The network helps show where Vincent Michalski may publish in the future.
Co-authorship network of co-authors of Vincent Michalski
This figure shows the co-authorship network connecting the top 25 collaborators of Vincent Michalski.
A scholar is included among the top collaborators of Vincent Michalski 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 Vincent Michalski. Vincent Michalski is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Deudon, Michel, Freddie Kalaitzis, Zhichao Lin, et al.. (2019). HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion.5 indexed citations
3.
Li, Raymond, Samira Ebrahimi Kahou, Hannes Schulz, et al.. (2018). Towards Deep Conversational Recommendations. PolyPublie (École Polytechnique de Montréal). 31. 9725–9735.64 indexed citations
Goyal, Raghav, Samira Ebrahimi Kahou, Vincent Michalski, et al.. (2017). The “Something Something” Video Database for Learning and Evaluating Visual Common Sense. 5843–5851.772 indexed citations breakdown →
6.
Kahou, Samira Ebrahimi, et al.. (2017). FigureQA: An Annotated Figure Dataset for Visual Reasoning. International Conference on Learning Representations.6 indexed citations
Kahou, Samira Ebrahimi, Vincent Michalski, Kishore Konda, Roland Memisevic, & Christopher Pal. (2015). Recurrent Neural Networks for Emotion Recognition in Video. PolyPublie (École Polytechnique de Montréal). 467–474.250 indexed citations
9.
Michalski, Vincent, Roland Memisevic, & Kishore Konda. (2014). Modeling Deep Temporal Dependencies with Recurrent Grammar Cells. Neural Information Processing Systems. 27. 1925–1933.39 indexed citations
10.
Konda, Kishore, Roland Memisevic, & Vincent Michalski. (2014). Learning to encode motion using spatio-temporal synchrony. International Conference on Learning Representations.6 indexed citations
11.
Konda, Kishore, Roland Memisevic, & Vincent Michalski. (2013). The role of spatio-temporal synchrony in the encoding of motion. arXiv (Cornell University).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.