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This 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.
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A 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.
Alimo, R., Sam, D., Lakhmiri, D., Kahovec, B., & Divsalar, D. (2021, March). Automated Data Accountability for Missions in Mars Rover Data [Paper]. IEEE Aerospace Conference (AeroConf 2021), Big Sky, MT, USA (8 pages). External link
Lakhmiri, D., & Le Digabel, S. (2022). Use of Static Surrogates in Hyperparameter Optimization. Operations Research Forum, 3(1). External link
Lakhmiri, D., Alimo, R., & Le Digabel, S. (2022). Anomaly detection for data accountability of Mars telemetry data. Expert Systems With Applications, 189, 116060. External link
Lakhmiri, D. (2021). Optimisation des hyperparamètres des réseaux de neurones profonds [Ph.D. thesis, Polytechnique Montréal]. Available
Lakhmiri, D., Le Digabel, S., & Tribes, C. (2021). HyperNOMAD: Hyperparameter Optimization of Deep Neural Networks Using Mesh Adaptive Direct Search. ACM Transactions on Mathematical Software, 47(3), 1-27. External link
Lakhmiri, D., & Le Digabel, S. (2021). Use of static surrogates in hyperparameter optimization. (Technical Report n° G-2021-10). External link
Lakhmiri, D., Alimo, R., & Le Digabel, S. (2020). Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system. (Technical Report n° 2020-31). External link
Lakhmiri, D. (2016). Un environnement pour l'optimisation sans dérivées [Master's thesis, École Polytechnique de Montréal]. Available