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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.
Gasse, M., & Lodi, A. (2022). Machine Learning for Combinatorial Optimization. In Pardalos, P. M., & Prokopyev, O. A. (eds.), Encyclopedia of Optimization (pp. 1-13). External link
Gupta, P., Gasse, M., Khalil, E. B., Kumar, M. P., Lodi, A., & Bengio, Y. (2020, December). Hybrid models for learning to branch [Paper]. 34th Conference on neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada (11 pages). External link
Gupta, P., Gasse, M., Khalil, E. B., Kumar, M. P., Lodi, A., & Bengio, Y. (2020, December). Supplement: Hybrid models for learning to branch [Paper]. 34th Conference on neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada (8 pages). External link
Gasse, M., Chetelat, D., Ferroni, N., Charlin, L., & Lodi, A. (2019, December). Exact Combinatorial Optimization with Graph Convolutional Neural Networks [Paper]. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, B.-C. (13 pages). External link
Müller, B., Muñoz, G., Gasse, M., Gleixner, A., Lodi, A., & Serrano, F. (2022). On generalized surrogate duality in mixed-integer nonlinear programming. Mathematical Programming, 2021(1-2), 1-30. Available
Milecki, L., Porée, J., Belgharbi, H., Bourquin, C., Damseh, R., Delafontaine-Martel, P., Lesage, F., Gasse, M., & Provost, J. (2021). A deep learning framework for spatiotemporal ultrasound localization microscopy. IEEE Transactions on Medical Imaging, 40(5), 1428-1437. External link
Müller, B., Muñoz, G., Gasse, M., Gleixner, A., Lodi, A., & Serrano, F. (2020, June). On Generalized Surrogate Duality in Mixed-Integer Nonlinear Programming [Paper]. 21st International Conference on Integer Programming and Combinatorial Optimization (IPCO 2020), London, United Kingdom. External link
Rastgar Amini, F., Contardo, C., Desaulniers, G., & Gasse, M. (2025). Learning to enumerate shifts for large-scale flexible personnel scheduling problems. Journal of Scheduling, 19 pages. External link
Rauby, B., Xing, P., Porée, J., Gasse, M., & Provost, J. (2025). Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy. IEEE Transactions on Image Processing, 34, 2367-2378. External link
Rauby, B., Xing, P., Gasse, M., & Provost, J. (2024). Deep learning in ultrasound localization microscopy : applications and perspectives. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 3462299 (23 pages). Available
Rastgar Amini, F., Contardo, C., Desaulniers, G., & Gasse, M. (2022). Learning to enumerate shifts for large-scale flexible personnel scheduling problems. (Technical Report n° G-2022-29). External link
Scavuzzo, L., Chetelat, D., Lodi, A., Chen, F. Y., Gasse, M., Yorke-Smith, N., & Aardal, K. (2022, November). Learning to Branch with Tree MDPs [Poster]. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, USA. External link
Scavuzzo, L., Chetelat, D., Lodi, A., Chen, F. Y., Gasse, M., Yorke-Smith, N., & Aardal, K. (2022, November). Learning to Branch with Tree MDPs [Presentation]. In 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, USA. External link
