Search Results for author: Miguel Couceiro

Found 12 papers, 3 papers with code

GECko+: a Grammatical and Discourse Error Correction Tool

1 code implementation JEP/TALN/RECITAL 2021 Eduardo Calò, Léo Jacqmin, Thibo Rosemplatt, Maxime Amblard, Miguel Couceiro, Ajinkya Kulkarni

GECko+ : a Grammatical and Discourse Error Correction Tool We introduce GECko+, a web-based writing assistance tool for English that corrects errors both at the sentence and at the discourse level.

Sentence Ordering

Tackling Morphological Analogies Using Deep Learning -- Extended Version

no code implementations9 Nov 2021 Safa Alsaidi, Amandine Decker, Esteban Marquer, Pierre-Alexandre Murena, Miguel Couceiro

We demonstrate our model's competitive performance on analogy detection and resolution over multiple languages.

On the Transferability of Neural Models of Morphological Analogies

no code implementations9 Aug 2021 Safa Alsaidi, Amandine Decker, Puthineath Lay, Esteban Marquer, Pierre-Alexandre Murena, Miguel Couceiro

Analogical proportions are statements expressed in the form "A is to B as C is to D" and are used for several reasoning and classification tasks in artificial intelligence and natural language processing (NLP).

A Neural Approach for Detecting Morphological Analogies

no code implementations9 Aug 2021 Safa Alsaidi, Amandine Decker, Puthineath Lay, Esteban Marquer, Pierre-Alexandre Murena, Miguel Couceiro

In fact, symbolic approaches were developed to solve or to detect analogies between character strings, e. g., the axiomatic approach as well as that based on Kolmogorov complexity.

Reducing Unintended Bias of ML Models on Tabular and Textual Data

no code implementations5 Aug 2021 Guilherme Alves, Maxime Amblard, Fabien Bernier, Miguel Couceiro, Amedeo Napoli

Unintended biases in machine learning (ML) models are among the major concerns that must be addressed to maintain public trust in ML.

Fairness

A Bayesian Convolutional Neural Network for Robust Galaxy Ellipticity Regression

no code implementations20 Apr 2021 Claire Theobald, Bastien Arcelin, Frédéric Pennerath, Brieuc Conan-Guez, Miguel Couceiro, Amedeo Napoli

We show that while a convolutional network can be trained to correctly estimate well calibrated aleatoric uncertainty, -- the uncertainty due to the presence of noise in the images -- it is unable to generate a trustworthy ellipticity distribution when exposed to previously unseen data (i. e. here, blended scenes).

A Bayesian Neural Network based on Dropout Regulation

no code implementations3 Feb 2021 Claire Theobald, Frédéric Pennerath, Brieuc Conan-Guez, Miguel Couceiro, Amedeo Napoli

Bayesian Neural Networks (BNN) have recently emerged in the Deep Learning world for dealing with uncertainty estimation in classification tasks, and are used in many application domains such as astrophysics, autonomous driving... BNN assume a prior over the weights of a neural network instead of point estimates, enabling in this way the estimation of both aleatoric and epistemic uncertainty of the model prediction. Moreover, a particular type of BNN, namely MC Dropout, assumes a Bernoulli distribution on the weights by using Dropout. Several attempts to optimize the dropout rate exist, e. g. using a variational approach. In this paper, we present a new method called "Dropout Regulation" (DR), which consists of automatically adjusting the dropout rate during training using a controller as used in automation. DR allows for a precise estimation of the uncertainty which is comparable to the state-of-the-art while remaining simple to implement.

Autonomous Driving

Making ML models fairer through explanations: the case of LimeOut

no code implementations1 Nov 2020 Guilherme Alves, Vaishnavi Bhargava, Miguel Couceiro, Amedeo Napoli

To illustrate, we will revisit the case of "LimeOut" that was proposed to tackle "process fairness", which measures a model's reliance on sensitive or discriminatory features.

Fairness

LimeOut: An Ensemble Approach To Improve Process Fairness

no code implementations17 Jun 2020 Vaishnavi Bhargava, Miguel Couceiro, Amedeo Napoli

To achieve both, we draw inspiration from "dropout" techniques in neural based approaches, and propose a framework that relies on "feature drop-out" to tackle process fairness.

Decision Making Fairness

Knowledge-Based Matching of $n$-ary Tuples

1 code implementation19 Feb 2020 Pierre Monnin, Miguel Couceiro, Amedeo Napoli, Adrien Coulet

In particular, units should be matched within and across sources, and their level of relatedness should be classified into equivalent, more specific, or similar.

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