5 citations · 5 across the 4 of their papers we have counts for
5 papers
Can Active Learning Preemptively Mitigate Fairness Issues?
Frédéric Branchaud-Charron, Parmida Atighehchian, Pau Rodríguez +2
Dataset bias is one of the prevailing causes of unfairness in machine learning. Addressing fairness at the data collection and dataset preparation stages therefore becomes an essen…
Fast High Resolution Blood Flow Estimation and Clutter Rejection via an Alternating Optimization Problem
Duong-Hung Pham, Adrian Basarab, Jean-Pierre Remenieras +2
This paper introduces a computationally efficient technique for estimating high-resolution Doppler blood flow from an ultrafast ultrasound image sequence. More precisely, it consis…
Synbols: Probing Learning Algorithms with Synthetic Datasets
Alexandre Lacoste, Pau Rodríguez, Frédéric Branchaud-Charron +7
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms. Enabling the design of datasets to te…
Embedding Propagation: Smoother Manifold for Few-Shot Classification
Pau Rodríguez, Issam Laradji, Alexandre Drouin +1
Few-shot classification is challenging because the data distribution of the training set can be widely different to the test set as their classes are disjoint. This distribution sh…
Cosmological Bianchi Class A models in Sáez-Ballester theory
J. Socorro, Paulo A. Rodríguez, Abraham Espinoza-García +2
We use the Sáez-Ballester (SB) theory on anisotropic Bianchi Class A cosmological model, with barotropic fluid and cosmological constant, using the Hamilton or Hamilton-Jacobi appr…