La Gueule et la Peau : le loup-garou médiéval en France et en Europe

Around the year 1000 AD, the word Werwolf ceased to be used as an antroponym to describe a man-wolf. Shortly afterwards, the French word garou appeared. The Middle Ages, then, constituted a turning point in the history of this monster. While werewolves had obviously existed prior to these early references, they have prompted me to enquire as to whether the generalisation of textual naming had participated in the development of a common definition of the monster which transcended the diversity of its representations. Did the recording in pen and ink of a name which had surely existed previously in the oral tradition contribute to the elaboration of the werewolf myth ? Relying on comparatism, literature and cultural studies, this thesis first seeks to explore the specificities of mediaeval werewolf literature, in which the figure of the werewolf seems to be exclusively gendered as male. Second, while recognising the incarnations of the blood-thirsty monster as problematic and unstable, this work adopts a diachronic perspective in order to reveal the commonality which underlies the multiplicity of werewolf figures.Vers l’an Mille, nous lisons les premières occurrences dans lesquelles le mot Werwolf cesse d’être un anthroponyme pour désigner un homme-loup. Peu de temps après « apparaît » le mot garou. Le Moyen Âge est donc une époque charnière pour l’histoire de ce monstre. S’il n’est, bien entendu, pas question d’affirmer que le loup-garou n’existait pas avant ces premières mentions, nous nous sommes posés la question de savoir si la généralisation d’une dénomination de la créature a contribué à fixer des constantes, au-delà de la diversité des manifestations particulières du monstre. La fixation d’un nom par l’écrit, qui existait sûrement déjà dans la tradition orale, a-t-elle mené à l’élaboration d’une dimension mythique du loup-garou ? En combinant le comparatisme, les études littéraires et les cultural studies, nous avons cherché, d’un côté, à déterminer les spécificités du corpus médiéval du loup-garou, dont les garous féminins semblent, a priori, absents. De l’autre, en adoptant une perspective diachronique, nous avons tenté de dégager une unité derrière la multiplicité que nous avons recensée des cas de loup-garou, de ce montre dévorant dont l’incarnation est problématique etinstable

The Teeth and the Skin : the werewolf in medieval literature in France and Europe

Vers l’an Mille, nous lisons les premières occurrences dans lesquelles le mot Werwolf cesse d’être un anthroponyme pour désigner un homme-loup. Peu de temps après « apparaît » le mot garou. Le Moyen Âge est donc une époque charnière pour l’histoire de ce monstre. S’il n’est, bien entendu, pas question d’affirmer que le loup-garou n’existait pas avant ces premières mentions, nous nous sommes posés la question de savoir si la généralisation d’une dénomination de la créature a contribué à fixer des constantes, au-delà de la diversité des manifestations particulières du monstre. La fixation d’un nom par l’écrit, qui existait sûrement déjà dans la tradition orale, a-t-elle mené à l’élaboration d’une dimension mythique du loup-garou ? En combinant le comparatisme, les études littéraires et les cultural studies, nous avons cherché, d’un côté, à déterminer les spécificités du corpus médiéval du loup-garou, dont les garous féminins semblent, a priori, absents. De l’autre, en adoptant une perspective diachronique, nous avons tenté de dégager une unité derrière la multiplicité que nous avons recensée des cas de loup-garou, de ce montre dévorant dont l’incarnation est problématique etinstable.Around the year 1000 AD, the word Werwolf ceased to be used as an antroponym to describe a man-wolf. Shortly afterwards, the French word garou appeared. The Middle Ages, then, constituted a turning point in the history of this monster. While werewolves had obviously existed prior to these early references, they have prompted me to enquire as to whether the generalisation of textual naming had participated in the development of a common definition of the monster which transcended the diversity of its representations. Did the recording in pen and ink of a name which had surely existed previously in the oral tradition contribute to the elaboration of the werewolf myth ? Relying on comparatism, literature and cultural studies, this thesis first seeks to explore the specificities of mediaeval werewolf literature, in which the figure of the werewolf seems to be exclusively gendered as male. Second, while recognising the incarnations of the blood-thirsty monster as problematic and unstable, this work adopts a diachronic perspective in order to reveal the commonality which underlies the multiplicity of werewolf figures

Using Wasserstein-2 regularization to ensure fair decisions with Neural-Network classifiers

In this paper, we propose a new method to build fair Neural-Network classifiers by using a constraint based on the Wasserstein distance. More specifically, we detail how to efficiently compute the gradients of Wasserstein-2 regularizers for Neural-Networks. The proposed strategy is then used to train Neural-Networks decision rules which favor fair predictions. Our method fully takes into account two specificities of Neural-Networks training: (1) The network parameters are indirectly learned based on automatic differentiation and on the loss gradients, and (2) batch training is the gold standard to approximate the parameter gradients, as it requires a reasonable amount of computations and it can efficiently explore the parameters space. Results are shown on synthetic data, as well as on the UCI Adult Income Dataset. Our method is shown to perform well compared with 'ZafarICWWW17' and linear-regression with Wasserstein-1 regularization, as in 'JiangUAI19', in particular when non-linear decision rules are required for accurate predictions

Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularization

The increasingly common use of neural network classifiers in industrial and social applications of image analysis has allowed impressive progress these last years. Such methods are however sensitive to algorithmic bias, i.e. to an under- or an over-representation of positive predictions or to higher prediction errors in specific subgroups of images. We then introduce in this paper a new method to temper the algorithmic bias in Neural-Network based classifiers. Our method is Neural-Network architecture agnostic and scales well to massive training sets of images. It indeed only overloads the loss function with a Wasserstein-2 based regularization term for which we back-propagate the impact of specific output predictions using a new model, based on the Gateaux derivatives of the predictions distribution. This model is algorithmically reasonable and makes it possible to use our regularized loss with standard stochastic gradient-descent strategies. Its good behavior is assessed on the reference Adult census, MNIST, CelebA datasets

Leveraging Influence Functions for Dataset Exploration and Cleaning

International audienceIn this paper, we tackle the problem of finding potentially problematic samples and complex regions of the input space for large pools of data without any supervision, with the objective of being relayed to and validated by a domain expert. This information can be critical, as even a low level of noise in the dataset may severely bias the model through spurious correlations between unrelated samples, and under-represented groups of data-points will exacerbate this issue. As such, we present two practical applications of influence functions in neural network models to industrial use-cases: exploration and cleanup of mislabeled examples in datasets. This robust statistics tool allows us to approximately know how different an estimator might be if we slightly changed the training dataset. In particular, we apply this technique to an ACAS Xu neural network surrogate model use-case[14] for complex region exploration, and to the CIFAR-10 canonical RGB image classification problem[20] for mislabeled sample detection with promising results

Leveraging Influence Functions for Dataset Exploration and Cleaning

International audienceIn this paper, we tackle the problem of finding potentially problematic samples and complex regions of the input space for large pools of data without any supervision, with the objective of being relayed to and validated by a domain expert. This information can be critical, as even a low level of noise in the dataset may severely bias the model through spurious correlations between unrelated samples, and under-represented groups of data-points will exacerbate this issue. As such, we present two practical applications of influence functions in neural network models to industrial use-cases: exploration and cleanup of mislabeled examples in datasets. This robust statistics tool allows us to approximately know how different an estimator might be if we slightly changed the training dataset. In particular, we apply this technique to an ACAS Xu neural network surrogate model use-case[14] for complex region exploration, and to the CIFAR-10 canonical RGB image classification problem[20] for mislabeled sample detection with promising results

Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks

We propose a new method, dubbed One Class Signed Distance Function (OCSDF), to perform One Class Classification (OCC) by provably learning the Signed Distance Function (SDF) to the boundary of the support of any distribution. The distance to the support can be interpreted as a normality score, and its approximation using 1-Lipschitz neural networks provides robustness bounds against l2 adversarial attacks, an underexplored weakness of deep learning-based OCC algorithms. As a result, OCSDF comes with a new metric, certified AUROC, that can be computed at the same cost as any classical AUROC. We show that OCSDF is competitive against concurrent methods on tabular and image data while being way more robust to adversarial attacks, illustrating its theoretical properties. Finally, as exploratory research perspectives, we theoretically and empirically show how OCSDF connects OCC with image generation and implicit neural surface parametrization. Our code is available at https://anonymous.4open. science/r/CSDFL

Expertise collective CRREF « Coupes Rases et Renouvellement des peuplements Forestiers en contexte de changement climatique » : Rapport scientifique de l’expertise: Rapport scientifique de l'expertise

Cette synthèse, ainsi que le rapport d’expertise et les exposés du séminaire de restitution, estdisponible sur le site web du GIP Ecofor (http://www.gip-ecofor.org/)