MIO-TCD: A New Benchmark Dataset for Vehicle Classification and Localization. - Zhiming Luo; Frederic Branchaud-Charron; Carl Lemaire; Janusz Konrad; Shaozi Li; Akshaya Mishra; Andrew Achkar; Justin A. Eichel; Pierre-Marc Jodoin (IEEE Trans. Image Process., 2018)

Spectral Metric for Dataset Complexity Assessment. - Frederic Branchaud-Charron; Andrew Achkar; Pierre-Marc Jodoin (CVPR, 2019)

Synbols: Probing Learning Algorithms with Synthetic Datasets. - Alexandre Lacoste; Pau Rodríguez López; Frederic Branchaud-Charron; Parmida Atighehchian; Massimo Caccia; Issam Hadj Laradji; Alexandre Drouin; Matt Craddock; Laurent Charlin; David Vázquez 001 (NeurIPS, 2020)

Bayesian active learning for production, a systematic study and a reusable library. - Parmida Atighehchian; Frédéric Branchaud-Charron; Alexandre Lacoste (CoRR, 2020)

A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images. - Issam H. Laradji; Pau Rodríguez; Frederic Branchaud-Charron; Keegan Lensink; Parmida Atighehchian; William Parker; David Vázquez 001; Derek Nowrouzezahrai (CoRR, 2020)

Can Active Learning Preemptively Mitigate Fairness Issues? - Frédéric Branchaud-Charron; Parmida Atighehchian; Pau Rodríguez; Grace Abuhamad; Alexandre Lacoste (CoRR, 2021)

Azimuth: Systematic Error Analysis for Text Classification. - Gabrielle Gauthier Melançon; Orlando Marquez Ayala; Lindsay Brin; Chris Tyler; Frederic Branchaud-Charron; Joseph Marinier; Karine Grande; Di Le (EMNLP (Demos), 2022)

Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning. - Andreas Kirsch 002; Sebastian Farquhar; Parmida Atighehchian; Andrew Jesson; Frédéric Branchaud-Charron; Yarin Gal (Trans. Mach. Learn. Res., 2023)