1 code implementation • 28 Feb 2024 • Fatemeh Nassajian Mojarrad, Lorenzo Bini, Thomas Matthes, Stéphane Marchand-Maillet
LeukoGraph intricately addresses a classification paradigm where for example four different cell populations undergo flat categorization, while a fifth diverges into two distinct child branches, exemplifying the nuanced hierarchical structure inherent in complex datasets.
1 code implementation • 28 Feb 2024 • Lorenzo Bini, Fatemeh Nassajian Mojarrad, Thomas Matthes, Stéphane Marchand-Maillet
To the best of our knowledge, this is the first effort to use GATs, and Graph Neural Networks (GNNs) in general, to classify cell populations from single-cell flow cytometry data.
1 code implementation • 28 Feb 2024 • Lorenzo Bini, Fatemeh Nassajian Mojarrad, Margarita Liarou, Thomas Matthes, Stéphane Marchand-Maillet
This paper presents FlowCyt, the first comprehensive benchmark for multi-class single-cell classification in flow cytometry data.
1 code implementation • 19 Jun 2023 • Yuan Chen, Stéphane Marchand-Maillet
Other existing methods construct the similarity graph and consider all points simultaneously.
no code implementations • 25 Jan 2022 • Etienne Brangbour, Pierrick Bruneau, Thomas Tamisier, Stéphane Marchand-Maillet
We present novel active learning strategies dedicated to providing a solution to the cold start stage, i. e. initializing the classification of a large set of data with no attached labels.
1 code implementation • 3 Jul 2021 • Lionel Blondé, Alexandros Kalousis, Stéphane Marchand-Maillet
Only our framework allowed us to design a method that performed well across the spectrum while remaining modular if more information about the quality of the data ever becomes available.
no code implementations • 1 Jun 2021 • Nicola Messina, Giuseppe Amato, Fabrizio Falchi, Claudio Gennaro, Stéphane Marchand-Maillet
It is designed for producing fixed-size 1024-d vectors describing whole images and sentences, as well as variable-length sets of 1024-d vectors describing the various building components of the two modalities (image regions and sentence words respectively).
no code implementations • 7 Dec 2020 • Etienne Brangbour, Pierrick Bruneau, Stéphane Marchand-Maillet, Renaud Hostache, Marco Chini, Patrick Matgen, Thomas Tamisier
In this paper, we investigate the conversion of a Twitter corpus into geo-referenced raster cells holding the probability of the associated geographical areas of being flooded.
1 code implementation • 12 Aug 2020 • Nicola Messina, Giuseppe Amato, Andrea Esuli, Fabrizio Falchi, Claudio Gennaro, Stéphane Marchand-Maillet
In this work, we tackle the task of cross-modal retrieval through image-sentence matching based on word-region alignments, using supervision only at the global image-sentence level.
Ranked #6 on Image Retrieval on Flickr30K 1K test
no code implementations • 12 Mar 2019 • Etienne Brangbour, Pierrick Bruneau, Stéphane Marchand-Maillet, Renaud Hostache, Patrick Matgen, Marco Chini, Thomas Tamisier
In this paper, we discuss the collection of a corpus associated to tropical storm Harvey, as well as its analysis from both spatial and topical perspectives.
1 code implementation • 16 May 2018 • Magda Gregorová, Alexandros Kalousis, Stéphane Marchand-Maillet
We investigate structured sparsity methods for variable selection in regression problems where the target depends nonlinearly on the inputs.
no code implementations • 19 Apr 2018 • Magda Gregorová, Jason Ramapuram, Alexandros Kalousis, Stéphane Marchand-Maillet
We propose a new method for input variable selection in nonlinear regression.
1 code implementation • 27 Jun 2017 • Magda Gregorová, Alexandros Kalousis, Stéphane Marchand-Maillet
Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series.
no code implementations • 14 Jan 2017 • Frank Nielsen, Ke Sun, Stéphane Marchand-Maillet
We describe a framework to build distances by measuring the tightness of inequalities, and introduce the notion of proper statistical divergences and improper pseudo-divergences.
no code implementations • 7 Jul 2015 • Magda Gregorova, Alexandros Kalousis, Stéphane Marchand-Maillet
We consider the problem of learning models for forecasting multiple time-series systems together with discovering the leading indicators that serve as good predictors for the system.