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Pierre-Antoine Manzagol

4 accepted papers

2021

PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair

Zimin Chen, Vincent Josua Hellendoorn, Pascal Lamblin, Petros Maniatis, Pierre-Antoine Manzagol, Daniel Tarlow +1
NeurIPS 2021spotlight

Machine learning for understanding and editing source code has recently attracted significant interest, with many developments in new models, new code representations, and new tasks. This proliferation can appear disparate and disconnected, making each approach seemingly unique and incompatible, thu…

Cited by 38Source ↗PDF ↗code ↗Scholar ↗
2020

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu +5
ICLR 2020poster

Few-shot classification refers to learning a classifier for new classes given only a few examples. While a plethora of models have emerged to tackle it, we find the procedure and datasets that are used to assess their progress lacking. To address this limitation, we propose Meta-Dataset: a new bench…

Cited by 788Source ↗code ↗Scholar ↗
2020

On the interplay between noise and curvature and its effect on optimization and generalization

Valentin Thomas, Fabian Pedregosa, Bart Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux
AISTATS 2020poster

The speed at which one can minimize an expected loss using stochastic methods depends on two properties: the curvature of the loss and the variance of the gradients. While most previous works focus on one or the other of these properties, we explore how their interaction affects optimization speed.…

Cited by 72Source ↗PDF ↗Scholar ↗
2019

Reducing the variance in online optimization by transporting past gradients

Sébastien Arnold, Pierre-Antoine Manzagol, Reza Babanezhad Harikandeh, Ioannis Mitliagkas, Nicolas Le Roux
NeurIPS 2019spotlight

Most stochastic optimization methods use gradients once before discarding them. While variance reduction methods have shown that reusing past gradients can be beneficial when there is a finite number of datapoints, they do not easily extend to the online setting. One issue is the staleness due to us…

Cited by 28Source ↗PDF ↗code ↗Scholar ↗