1 code implementation • SMM4H (COLING) 2022 • Aman Sinha, Cristina Garcia Holgado, Marianne Clausel, Matthieu Constant
Biomedical NER is an active research area today.
no code implementations • 15 Sep 2023 • Rohit Agarwal, Aman Sinha, Dilip K. Prasad, Marianne Clausel, Alexander Horsch, Mathieu Constant, Xavier Coubez
Modelling irregularly-sampled time series (ISTS) is challenging because of missing values.
no code implementations • 26 Jun 2022 • Julien Flamant, Konstantin Usevich, Marianne Clausel, David Brie
This work introduces a novel Fourier phase retrieval model, called polarimetric phase retrieval that enables a systematic use of polarization information in Fourier phase retrieval problems.
no code implementations • 20 Apr 2022 • Dimitri Bouche, Rémi Flamary, Florence d'Alché-Buc, Riwal Plougonven, Marianne Clausel, Jordi Badosa, Philippe Drobinski
We study short-term prediction of wind speed and wind power (every 10 minutes up to 4 hours ahead).
1 code implementation • 26 Feb 2022 • Aleksandra Burashnikova, Yury Maximov, Marianne Clausel, Charlotte Laclau, Franck Iutzeler, Massih-Reza Amini
This paper is an extended version of [Burashnikova et al., 2021, arXiv: 2012. 06910], where we proposed a theoretically supported sequential strategy for training a large-scale Recommender System (RS) over implicit feedback, mainly in the form of clicks.
no code implementations • 4 Dec 2021 • Aleksandra Burashnikova, Marianne Clausel, Massih-Reza Amini, Yury Maximov, Nicolas Dante
In this paper, we study the effect of long memory in the learnability of a sequential recommender system including users' implicit feedback.
no code implementations • 30 Nov 2021 • Georgios Balikas, Massih-Reza Amini, Marianne Clausel
However, this assumption is strong for comparable corpora that consist of documents thematically similar to an extent only, which are, in turn, the most commonly available or easy to obtain.
1 code implementation • 12 Dec 2020 • Aleksandra Burashnikova, Marianne Clausel, Charlotte Laclau, Frack Iutzeller, Yury Maximov, Massih-Reza Amini
In this paper, we propose a theoretically founded sequential strategy for training large-scale Recommender Systems (RS) over implicit feedback, mainly in the form of clicks.
no code implementations • NeurIPS 2020 • Sami Alkhoury, Emilie Devijver, Marianne Clausel, Myriam Tami, Eric Gaussier, Georges Oppenheim
We propose here a generalization of regression trees, referred to as Probabilistic Regression (PR) trees, that adapt to the smoothness of the prediction function relating input and output variables while preserving the interpretability of the prediction and being robust to noise.
no code implementations • 3 Mar 2020 • Dimitri Bouche, Marianne Clausel, François Roueff, Florence d'Alché-Buc
Then, in the more general setting of integral losses based on differentiable ground losses, KPL is implemented using first-order optimization for both fully and partially observed output functions.
no code implementations • 27 Oct 2018 • Myriam Tami, Marianne Clausel, Emilie Devijver, Adrien Dulac, Eric Gaussier, Stefan Janaqi, Meriam Chebre
Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression in many applications and research studies.
1 code implementation • ACL 2017 • Hesam Amoualian, Wei Lu, Eric Gaussier, Georgios Balikas, Massih R. Amini, Marianne Clausel
This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words.
1 code implementation • COLING 2016 • Georgios Balikas, Hesam Amoualian, Marianne Clausel, Eric Gaussier, Massih R. Amini
The exchangeability assumption in topic models like Latent Dirichlet Allocation (LDA) often results in inferring inconsistent topics for the words of text spans like noun-phrases, which are usually expected to be topically coherent.
1 code implementation • 1 Jun 2016 • Georgios Balikas, Massih-Reza Amini, Marianne Clausel
Probabilistic topic models are generative models that describe the content of documents by discovering the latent topics underlying them.