Search Results for author:
Found 10 papers, 6 papers with code
Match-And-Deform: Time Series Domain Adaptation through Optimal Transport and Temporal Alignment
While large volumes of unlabeled data are usually available, associated labels are often scarce.
Time Series Alignment with Global Invariances
Multivariate time series are ubiquitous objects in signal processing.
Early Classification for Agricultural Monitoring from Satellite Time Series
In this work, we introduce a recently developed early classification mechanism to satellite-based agricultural monitoring.
Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization
Times series classification can be successfully tackled by jointly learning a shapelet-based representation of the series in the dataset and classifying the series according to this representation.
Sliced Gromov-Wasserstein
Recently used in various machine learning contexts, the Gromov-Wasserstein distance (GW) allows for comparing distributions whose supports do not necessarily lie in the same metric space.
End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping
In this work, we present an End-to-End Learned Early Classification of Time Series (ELECTS) model that estimates a classification score and a probability of whether sufficient data has been observed to come to an early and still accurate decision.
Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties
Optimal transport theory has recently found many applications in machine learning thanks to its capacity for comparing various machine learning objects considered as distributions.
From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers
We find that whilst ensembling is a key component for both algorithms, the effect of the other components is mixed and more complex.
Optimal Transport for structured data with application on graphs
This work considers the problem of computing distances between structured objects such as undirected graphs, seen as probability distributions in a specific metric space.
Ranked #3 on
Graph Classification
on NCI1
Dense Bag-of-Temporal-SIFT-Words for Time Series Classification
In this paper, we design a time series classification scheme that builds on the SIFT framework adapted to time series to feed a Bag-of-Words.
