no code implementations • 25 Oct 2023 • Etienne Le Naour, Ghislain Agoua, Nicolas Baskiotis, Vincent Guigue
In this work, we propose a set of requirements for a neural representation of univariate time series to be interpretable.
1 code implementation • 9 Jun 2023 • Etienne Le Naour, Louis Serrano, Léon Migus, Yuan Yin, Ghislain Agoua, Nicolas Baskiotis, Patrick Gallinari, Vincent Guigue
We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data, such as irregular samples, missing data, or unaligned measurements from multiple sensors.
1 code implementation • 1 Feb 2022 • Matthieu Kirchmeyer, Yuan Yin, Jérémie Donà, Nicolas Baskiotis, Alain Rakotomamonjy, Patrick Gallinari
Data-driven approaches to modeling physical systems fail to generalize to unseen systems that share the same general dynamics with the learning domain, but correspond to different physical contexts.
1 code implementation • NeurIPS 2021 • Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Baskiotis, Patrick Gallinari
Both are sub-optimal: the former disregards the discrepancies between environments leading to biased solutions, while the latter does not exploit their potential commonalities and is prone to scarcity problems.
2 code implementations • 2 Jul 2019 • Thomas Gerald, Hadi Zaatiti, Hatem Hajri, Nicolas Baskiotis, Olivier Schwander
Considering the success of hyperbolic representations of graph-structured data in last years, an ongoing challenge is to set up a hyperbolic approach for the community detection problem.
no code implementations • 24 Jun 2019 • Thomas Gerald, Aurélia Léon, Nicolas Baskiotis, Ludovic Denoyer
Different models based on the notion of binary codes have been proposed to overcome this limitation, achieving in a sublinear inference complexity.
no code implementations • 30 Mar 2015 • Ioannis Partalas, Aris Kosmopoulos, Nicolas Baskiotis, Thierry Artieres, George Paliouras, Eric Gaussier, Ion Androutsopoulos, Massih-Reza Amini, Patrick Galinari
LSHTC is a series of challenges which aims to assess the performance of classification systems in large-scale classification in a a large number of classes (up to hundreds of thousands).
no code implementations • NeurIPS 2010 • Emile Richard, Nicolas Baskiotis, Theodoros Evgeniou, Nicolas Vayatis
We consider the problem of discovering links of an evolving undirected graph given a series of past snapshots of that graph.