Pascal Lamblin — accepted AI-conference 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 38 Source ↗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 788 Source ↗code ↗Scholar ↗

2018

Automatic differentiation in ML: Where we are and where we should be going

Bart van Merrienboer, Olivier Breuleux, Arnaud Bergeron, Pascal Lamblin

NeurIPS 2018spotlight

We review the current state of automatic differentiation (AD) for array programming in machine learning (ML), including the different approaches such as operator overloading (OO) and source transformation (ST) used for AD, graph-based intermediate representations for programs, and source languages.…

Cited by 109 Source ↗PDF ↗code ↗Scholar ↗