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Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
Use the mouse wheel or scroll gestures to zoom into the graph.
You can click on the nodes and links to highlight them and move the nodes by dragging them.
Hold down the "Ctrl" key or the "⌘" key while clicking on the nodes to open the list of this person's publications.
Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Kahou, S. E., Michalski, V., Memisevic, R., Pal, C. J., & Vincent, P. (2017, July). RATM: Recurrent Attentive Tracking Model [Paper]. 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2017), Honolulu, HI, United states. External link
Kahou, S. E., Bouthillier, X., Lamblin, P., Gulcehre, C., Michalski, V., Konda, K., Jean, S., Froumenty, P., Dauphin, Y., Boulanger-Lewandowski, N., Ferrari, R. C., Mirza, M., Warde-Farley, D., Courville, A., Vincent, P., Memisevic, R., Pal, C. J., & Bengio, Y. (2016). EmoNets: Multimodal deep learning approaches for emotion recognition in video. Journal on Multimodal User Interfaces, 10(2), 99-111. External link
Kahou, S. E., Michalski, V., Konda, K., Memisevic, R., & Pal, C. J. (2015, November). Recurrent Neural Networks for Emotion Recognition in Video [Paper]. 17th International Conference on Multimodal Interaction (ICMI 2015), Seattle, USA. External link
Kahou, S. E., Pal, C. J., Bouthillier, X., Froumenty, P., Gulcehre, C., Memisevic, R., Vincent, P., Courville, A., Bengio, Y., Ferrari, R. C., Mirza, M., Jean, S., Carrier, P.-L., Dauphin, Y., Boulanger-Lewandowski, N., Aggarwal, A., Zumer, J., Lamblin, P., Raymond, J.-P., ... Wu, Z. (2013, December). Combining modality specific deep neural networks for emotion recognition in video [Paper]. 15th ACM International Conference on Multimodal Interaction (ICMI 2013), Sydney, NSW, Australia. External link
