41 citations · 53 across the 6 of their papers we have counts for
6 papers · 1 filter
Universal hidden monotonic trend estimation with contrastive learning
Edouard Pineau, Sébastien Razakarivony
In this paper, we describe a universal method for extracting the underlying monotonic trend factor from time series data. We propose an approach related to the Mann-Kendall test, a…
Time Series Source Separation with Slow Flows
Edouard Pineau, Sébastien Razakarivony, Thomas Bonald
In this paper, we show that slow feature analysis (SFA), a common time series decomposition method, naturally fits into the flow-based models (FBM) framework, a type of invertible…
Using Laplacian Spectrum as Graph Feature Representation
Edouard Pineau
Graphs possess exotic features like variable size and absence of natural ordering of the nodes that make them difficult to analyze and compare. To circumvent this problem and learn…
Variational Recurrent Neural Networks for Graph Classification
Edouard Pineau, Nathan de Lara
We address the problem of graph classification based only on structural information. Inspired by natural language processing techniques (NLP), our model sequentially embeds informa…
A Simple Baseline Algorithm for Graph Classification
Nathan de Lara, Edouard Pineau
Graph classification has recently received a lot of attention from various fields of machine learning e.g. kernel methods, sequential modeling or graph embedding. All these approac…
InfoCatVAE: Representation Learning with Categorical Variational Autoencoders
Edouard Pineau, Marc Lelarge
This paper describes InfoCatVAE, an extension of the variational autoencoder that enables unsupervised disentangled representation learning. InfoCatVAE uses multimodal distribution…