Philippe Very

According to our database, Philippe Very authored at least 12 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

On the Detection of Aircraft Single Engine Taxi using Deep Learning Models.

Gabriel Jarry, Philippe Very, Ramon Dalmau, Daniel Delahaye, Arthur Houdant

CoRR, 2024

On the Generalization Properties of Deep Learning for Aircraft Fuel Flow Estimation Models.

Gabriel Jarry, Ramon Dalmau, Philippe Very, Junzi Sun

CoRR, 2024

2021

Stochastic Adversarial Gradient Embedding for Active Domain Adaptation.

Victor Bouvier, Philippe Very, Clément Chastagnol, Myriam Tami, Céline Hudelot

Proceedings of the Workshop on Interactive Adaptive Learning (IAL 2021) co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021), 2021

Robust Domain Adaptation: Representations, Weights and Inductive Bias (Extended Abstract).

Victor Bouvier, Philippe Very, Clément Chastagnol, Myriam Tami, Céline Hudelot

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

2020

Robust Domain Adaptation: Representations, Weights and Inductive Bias.

Victor Bouvier, Philippe Very, Clément Chastagnol, Myriam Tami, Céline Hudelot

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

2019

Learning Invariant Representations for Sentiment Analysis: The Missing Material is Datasets.

Victor Bouvier, Philippe Very, Céline Hudelot, Clément Chastagnol

CoRR, 2019

Hidden Covariate Shift: A Minimal Assumption For Domain Adaptation.

Victor Bouvier, Philippe Very, Céline Hudelot, Clément Chastagnol

CoRR, 2019

2016

Toward a generic representation of random variables for machine learning.

Philippe Donnat, Gautier Marti, Philippe Very

Pattern Recognit. Lett., 2016

2015

Comment partitionner automatiquement des marches aléatoires ? Avec application à la finance quantitative.

Gautier Marti, Frank Nielsen, Philippe Very, Philippe Donnat

CoRR, 2015

HCMapper: An interactive visualization tool to compare partition-based flat clustering extracted from pairs of dendrograms.

Gautier Marti, Philippe Donnat, Frank Nielsen, Philippe Very

CoRR, 2015

A Proposal of a Methodological Framework with Experimental Guidelines to Investigate Clustering Stability on Financial Time Series.

Gautier Marti, Philippe Very, Philippe Donnat, Frank Nielsen

Proceedings of the 14th IEEE International Conference on Machine Learning and Applications, 2015

Clustering Random Walk Time Series.

Gautier Marti, Frank Nielsen, Philippe Very, Philippe Donnat

Proceedings of the Geometric Science of Information - Second International Conference, 2015