Pierre-Antoine Manzagol

Orcid: 0009-0009-5047-6369

According to our database1, Pierre-Antoine Manzagol authored at least 17 papers between 2007 and 2024.

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

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

Online presence:

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Bibliography

2024
Resolving Code Review Comments with Machine Learning.
Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice, 2024

2021
PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Learning to Fix Build Errors with Graph2Diff Neural Networks.
Proceedings of the ICSE '20: 42nd International Conference on Software Engineering, Workshops, Seoul, Republic of Korea, 27 June, 2020

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples.
Proceedings of the 8th International Conference on Learning Representations, 2020

On the interplay between noise and curvature and its effect on optimization and generalization.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Information matrices and generalization.
CoRR, 2019

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples.
CoRR, 2019

Reducing the variance in online optimization by transporting past gradients.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
Online variance-reducing optimization.
Proceedings of the 6th International Conference on Learning Representations, 2018

Negative eigenvalues of the Hessian in deep neural networks.
Proceedings of the 6th International Conference on Learning Representations, 2018

2016
Theano: A Python framework for fast computation of mathematical expressions.
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CoRR, 2016

2010
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion.
J. Mach. Learn. Res., 2010

Why Does Unsupervised Pre-training Help Deep Learning?
J. Mach. Learn. Res., 2010

2009
The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

2008
On the Use of Sparce Time Relative Auditory Codes for Music.
Proceedings of the ISMIR 2008, 2008

Extracting and composing robust features with denoising autoencoders.
Proceedings of the Machine Learning, 2008

2007
Topmoumoute Online Natural Gradient Algorithm.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007


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