Quentin Fournier

Orcid: 0000-0002-1036-0777

According to our database1, Quentin Fournier authored at least 13 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Predicting the Impact of Model Expansion through the Minima Manifold: A Loss Landscape Perspective.
CoRR, 2024

Exploring Quantization for Efficient Pre-Training of Transformer Language Models.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques.
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

2023
Energy and carbon-aware initial VM placement in geographically distributed cloud data centers.
Sustain. Comput. Informatics Syst., September, 2023

Detection of microservice-based software anomalies based on OpenTracing in cloud.
Softw. Pract. Exp., August, 2023

Distributed computation of the critical path from execution traces.
Softw. Pract. Exp., August, 2023

A Practical Survey on Faster and Lighter Transformers.
ACM Comput. Surv., 2023

Language Models for Novelty Detection in System Call Traces.
CoRR, 2023

2021
Automated Cause Analysis of Latency Outliers Using System-Level Dependency Graphs.
Proceedings of the 21st IEEE International Conference on Software Quality, 2021

On Improving Deep Learning Trace Analysis with System Call Arguments.
Proceedings of the 18th IEEE/ACM International Conference on Mining Software Repositories, 2021

2020
DepGraph: Localizing Performance Bottlenecks in Multi-Core Applications Using Waiting Dependency Graphs and Software Tracing.
Proceedings of the 20th IEEE International Working Conference on Source Code Analysis and Manipulation, 2020

2019
Automatic Cause Detection of Performance Problems in Web Applications.
Proceedings of the IEEE International Symposium on Software Reliability Engineering Workshops, 2019

Empirical Comparison between Autoencoders and Traditional Dimensionality Reduction Methods.
Proceedings of the 2nd IEEE International Conference on Artificial Intelligence and Knowledge Engineering, 2019


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