Empirical Comparison between Autoencoders and Traditional Dimensionality Reduction Methods. - Quentin Fournier; Daniel Aloise (AIKE, 2019)

Automatic Cause Detection of Performance Problems in Web Applications. - Quentin Fournier; Naser Ezzati-Jivan; Daniel Aloise; Michel R. Dagenais (ISSRE Workshops, 2019)

DepGraph: Localizing Performance Bottlenecks in Multi-Core Applications Using Waiting Dependency Graphs and Software Tracing. - Naser Ezzati-Jivan; Quentin Fournier; Michel R. Dagenais; Abdelwahab Hamou-Lhadj (SCAM, 2020)

On Improving Deep Learning Trace Analysis with System Call Arguments. - Quentin Fournier; Daniel Aloise; Seyed Vahid Azhari; François Tetreault (MSR, 2021)

Automated Cause Analysis of Latency Outliers Using System-Level Dependency Graphs. - Sneh Patel; Brendan Park; Naser Ezzati-Jivan; Quentin Fournier (QRS, 2021)

Language Models for Novelty Detection in System Call Traces. - Quentin Fournier; Daniel Aloise; Leandro Rincon Costa (CoRR, 2023)

A Practical Survey on Faster and Lighter Transformers. - Quentin Fournier; Gaétan Marceau Caron; Daniel Aloise (ACM Comput. Surv., 2023)

Distributed computation of the critical path from execution traces. - Pierre-Frédérick Denys; Quentin Fournier; Michel R. Dagenais (Softw. Pract. Exp., 2023)

Detection of microservice-based software anomalies based on OpenTracing in cloud. - Mohammad Khanahmadi; Alireza Shameli-Sendi; Masoume Jabbarifar; Quentin Fournier; Michel R. Dagenais (Softw. Pract. Exp., 2023)

Energy and carbon-aware initial VM placement in geographically distributed cloud data centers. - Ehsan Khodayarseresht; Alireza Shameli-Sendi; Quentin Fournier; Michel R. Dagenais (Sustain. Comput. Informatics Syst., 2023)

A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques. - Megh Thakkar; Quentin Fournier; Matthew Riemer; Pin-Yu Chen; Amal Zouaq; Payel Das; Sarath Chandar (ACL (1), 2024)

Exploring Quantization for Efficient Pre-Training of Transformer Language Models. - Kamran Chitsaz; Quentin Fournier; Gonçalo Mordido; Sarath Chandar (EMNLP (Findings), 2024)

Predicting the Impact of Model Expansion through the Minima Manifold: A Loss Landscape Perspective. - Pranshu Malviya; Jerry Huang; Quentin Fournier; Sarath Chandar (CoRR, 2024)

Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs. - Megh Thakkar; Yash More; Quentin Fournier; Matthew Riemer; Pin-Yu Chen; Amal Zouaq; Payel Das; Sarath Chandar (CoRR, 2024)

Small Encoders Can Rival Large Decoders in Detecting Groundedness. - Istabrak Abbes; Gabriele Prato 001; Quentin Fournier; Fernando Rodriguez; Alaa Boukhary; Adam Elwood; Sarath Chandar (ACL (Findings), 2025)

NeoBERT: A Next Generation BERT. - Lola Le Breton; Quentin Fournier; John Xavier Morris; Mariam El Mezouar; Sarath Chandar (Trans. Mach. Learn. Res., 2025)

NeoBERT: A Next-Generation BERT. - Lola Le Breton; Quentin Fournier; Mariam El Mezouar; Sarath Chandar (CoRR, 2025)

Structure-Aligned Protein Language Model. - Can Chen 005; David Heurtel-Depeiges; Robert M. Vernon; Christopher James Langmead; Yoshua Bengio; Quentin Fournier (CoRR, 2025)

CADmium: Fine-Tuning Code Language Models for Text-Driven Sequential CAD Design. - Prashant Govindarajan; Davide Baldelli; Jay Pathak; Quentin Fournier; Sarath Chandar (CoRR, 2025)