Search Results for author: Nicolas Baskiotis

Found 8 papers, 4 papers with code

Interpretable time series neural representation for classification purposes

no code implementations25 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.

Classification Representation Learning +1

Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations

1 code implementation9 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.

Imputation Meta-Learning +1

Generalizing to New Physical Systems via Context-Informed Dynamics Model

1 code implementation1 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.

LEADS: Learning Dynamical Systems that Generalize Across Environments

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.

From Node Embedding To Community Embedding : A Hyperbolic Approach

2 code implementations2 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.

Community Detection Graph Embedding

Binary Stochastic Representations for Large Multi-class Classification

no code implementations24 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.

Classification General Classification +1

LSHTC: A Benchmark for Large-Scale Text Classification

no code implementations30 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).

General Classification text-classification +1

Link Discovery using Graph Feature Tracking

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.

Matrix Completion

Cannot find the paper you are looking for? You can Submit a new open access paper.