1,721,088 research outputs found
De l'optimisation à la différentiation d'algorithmes : un détour par les graphes
No full textThis manuscript highlights the work of the author since he was nominated as "Chargé de Recherche" (research scientist) at Centre national de la recherche scientifique (CNRS) in 2015. In particular, the author shows a thematic and chronological evolution of his research interests:- The first part, following his post-doctoral work, is concerned with the development of new algorithms for non-smooth optimization.- The second part is the heart of his research in 2020. It is focused on the analysis of machine learning methods for graph (signal) processing.- Finally, the third and last part, oriented towards the future, is concerned with (automatic or not) differentiation of algorithms for learning and signal processing.Ce manuscript présente le travail de l'auteur depuis sa nomination comme "Chargé de Recherche" au Centre national de la recherche scientifique (CNRS) en 2015. En particulier, l'auteur dresse une évolution thématique et chronologique de ses intérêts de recherche :- La premier bloc, continuité de son travail post-doctoral, concerne le développement de nouveaux algorithmes pour l'optimisation non-lisse.- Le second lui est le coeur de recherche en 2020 pour l'auteur, à savoir l'analyse de méthodes d'apprentissage automatique pour le traitement de (signaux sur) graphes.- Enfin, le troisième et dernier bloc, tourné vers le futur, concerne la différentiation (automatique ou non) d'algorithmes en apprentissage et traitement du signal
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Graph learning with bilevel optimization
No full textCette thèse se concentre sur l’apprentissage de graphes pour les tâches d’apprentissage semi-supervisé afin d’atténuer l’impact du bruit dans les graphes du monde réel. Une approche pour apprendre les graphes est d’utiliser l’optimisation bi-niveau, dont le problème interne optimise le modèle en aval, et son problème externe évalue la performance du modèle optimisé par rapport à une perte d’étiquetage et met à jour le graphe en conséquence. Ce problème est en général numériquement intractable. Une solution consiste à remplacer l’optimiseur interne par la sortie d’un algorithme itératif convergeant vers un bon proxy, puis à utiliser la différentiation automatique pour évaluer sa dérivée par rapport au graphe, qui est appris à l’aide d’un algorithme basé sur le gradient. Dans cette thèse, nous proposons d’abord d’appliquer cette approche pour apprendre les priorités d’analyse-parcimonie, ce qui revient à un problème d’apprentissage de graphe dans les applications liées à la variation totale de graphe. Bien que le problème soit non-lisse, nous prouvons empiriquement la capacité de ce solveur dans les tâches de débruitage de signaux 1D et 2D. Nous proposons ensuite d’utiliser l’optimisation bi-niveau pour entraîner un modèle paramétrique sur la prédiction de la similitude entre les nœuds, au lieu d’apprendre directement le graphe. Nous montrons que cela améliore notablement les performances par rapport aux graphes observés. Enfin, nous identifions et analysons le problème de gradient scarcity, qui consiste en un manque de supervision sur les arêtes reliant des nœuds non étiquetés éloignés. Nous prouvons que ce problème émerge lors de l’optimisation directe des arêtes observées tout en utilisant des réseaux de neurones graphiques ou la régularisation laplacienne dans la tâche en aval. Nous examinons plusieurs solutions à ce problème, notamment l’apprentissage métrique, la régularisation de graphe ou l’expansion du graphe, et prouvons leur efficacité.This thesis focuses on graph learning for semi-supervised learning tasks to mitigate the impact of noise in real-world graphs.One approach to learn graphs is using bilevel optimization, whose inner problem optimizes the downstream model, and its outer problem evaluates the performance of the optimized model with respect to a labelling loss and updates the graph accordingly.This problem is intractable in general.One solution is replacing the inner optimizer by the output of an iterative algorithm converging to a good proxy, and then employing automatic differentiation to evaluate its derivative with respect to the graph, which is learned using a gradient-based algorithm.In this thesis, we first propose to apply this approach to learn analysis-sparsity priors, which boils down to a graph learning problem in applications related to graph Total Variation.Although the problem is non-smooth, we empirically prove the capacity of this solver in 1D and 2D signal denoising tasks.We then propose to use bilevel optimization to train a parametric model on predicting similarity between nodes, instead of learning the graph directly.We show that this notably improves performance over observed graphs.Finally, we identify and analyze the gradient scarcity problem, which consists in a lack of supervision on edges connecting distant unlabelled nodes.We prove that this issue emerges when directly optimizing the observed edges while using graph neural networks or the Laplacian regularization in the downstream task.We examine several solutions to this issue including metric learning, graph regularization, or expanding the graph, and prove their efficiency
Régularisations de faible complexité pour les problèmes inverses
No full textThis thesis is concerned with recovery guarantees and sensitivity analysis of variational regularization for noisy linear inverse problems. This is cast as aconvex optimization problem by combining a data fidelity and a regularizing functional promoting solutions conforming to some notion of low complexity related to their non-Smoothness points. Our approach, based on partial smoothness, handles a variety of regularizers including analysis/structured sparsity, antisparsity and low-Rank structure. We first give an analysis of thenoise robustness guarantees, both in terms of the distance of the recovered solutions to the original object, as well as the stability of the promoted modelspace. We then turn to sensivity analysis of these optimization problems to observation perturbations. With random observations, we build un biased estimator of the risk which provides a parameter selection scheme.Cette thèse se consacre aux garanties de reconstruction et de l’analyse de sensibilité de régularisation variationnelle pour des problèmes inverses linéaires bruités. Il s’agit d’un problème d’optimisation convexe combinant un terme d’attache aux données et un terme de régularisation promouvant des solutions vivant dans un espace dit de faible complexité. Notre approche, basée sur la notion de fonctions partiellement lisses, permet l’étude d’une grande variété de régularisations comme par exemple la parcimonie de type analyse ou structurée, l’anti-Parcimonie et la structure de faible rang. Nous analysons tout d’abord la robustesse au bruit, à la fois en termes de distance entre les solutions et l’objet original, ainsi que la stabilité de l’espace modèle promu.Ensuite, nous étudions la stabilité de ces problèmes d’optimisation à des perturbations des observations. A partir d’observations aléatoires, nous construisons un estimateur non biaisé du risque afin d’obtenir un schéma de sélection de paramètre
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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