 | Up a level |
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.
Chitsaz, K., Fournier, Q., Torcato Mordido, G. F., & Anbil Parthipan, S. C. (2024, November). Exploring Quantization for Efficient Pre-Training of Transformer Language Models [Paper]. Conference on Empirical Methods in Natural Language Processing (EMNLP 2024), Miami, FL, USA. External link
Denys, P.-F., Fournier, Q., & Dagenais, M. (2023). Distributed computation of the critical path from execution traces. Software: Practice and Experience, 53(8), 1722-1737. External link
Ezzati-Jivan, N., Fournier, Q., Dagenais, M., & Hamou-Lhadj, A. (2020, September). DepGraph: Localizing Performance Bottlenecks in Multi-Core Applications Using Waiting Dependency Graphs and Software Tracing [Paper]. 2020 IEEE 20th International Working Conference on Source Code Analysis and Manipulation (SCAM 2020), Adelaide, Australia. External link
Fournier, Q., Aloise, D., & Costa, L. R. (2023). Language Models for Novelty Detection in Kernel Traces [Dataset]. External link
Fournier, Q., Caron, G. M., & Aloise, D. (2023). A Practical Survey on Faster and Lighter Transformers. ACM Computing Surveys, 55(14s), 1-40. External link
Fournier, Q. (2022). Machine Learning for Anomaly Detection in Kernel Traces [Ph.D. thesis, Polytechnique Montréal]. Available
Fournier, Q., Aloise, D., Azhari, S. V., & Tétreault, F. (2021, May). On improving deep learning trace analysis with system call arguments [Paper]. 18th IEEE/ACM International Conference on Mining Software Repositories (MSR 2021), Madrid, Spain. External link
Fournier, Q., Ezzati-Jivan, N., Aloise, D., & Dagenais, M. (2019, October). Automatic cause detection of performance problems in web applications [Paper]. 30th IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW 2019), Berlin, Germany. External link
Fournier, Q., & Aloise, D. (2019, June). Empirical Comparison between Autoencoders and Traditional Dimensionality Reduction Methods [Paper]. IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE 2019), Sardinia, Italy. External link
Khanahmadi, M., Shameli-Sendi, A., Jabbarifar, M., Fournier, Q., & Dagenais, M. (2023). Detection of microservice-based software anomalies based on OpenTracing in cloud. Software: Practice and Experience, 53(8), 1681-1699. External link
Khodayarseresht, E., Shameli-Sendi, A., Fournier, Q., & Dagenais, M. (2023). Energy and carbon-aware initial VM placement in geographically distributed cloud data centers. Sustainable Computing: Informatics and Systems, 39, 11 pages. External link
Patel, S., Park, B., Ezzati-jivan, N., & Fournier, Q. (2021, December). Automated Cause Analysis of Latency Outliers Using System-Level Dependency Graphs [Paper]. 2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS 2021), Hainan, China. External link
