CORNELI , Marco

We create a framework to analyze the timing and frequency of instantaneous interactions between pairs of entities. This type of interaction data is especially common nowadays, and easily available. Examples of instantaneous interactions include email networks, phone call networks and some common types of technological and transportation...

We consider mixtures of longitudinal trajectories, where one trajectory contains measurements over time of the variable of interest for one individual and each individual belongs to one cluster. The number of clusters as well as individual cluster memberships are unknown and must be inferred. We propose an original Bayesian clustering framework...

L'objectif général de ce projet est d'adopter, adapter et combiner des méthodes de traitement automatique de la langue, de représentation des connaissances et raisonnement et d'apprentissage automatique pour analyser, classifier et automatiser l'annotation sémantique de textes anciens. • Pour en savoir plus sur ce projet soutenu par l'Académie...

We consider here the problem of co-clustering count matrices with a high level of missing values that may evolve along the time. We introduce a generative model, named dynamic latent block model (dLBM), to handle this situation and which extends the classical binary latent block model (LBM) to the dynamic case. The modeling of the dynamic time...

Nous considérons le problème du co-clustering des matrices binaires qui peuvent évoluer dans le temps et nous introduisons un modèle génératif pour le gérer. Le modèle proposé, appelé dynamic latent block model, étend le modèle des blocs latents binaire classique au cas dynamique. La modélisation de la dynamique en temps continu repose sur un...

This paper is about the co-clustering of ordinal data. Such data are very common on e-commerce platforms where customers rank the products/services they bought. More in details, we focus on arrays of ordinal (possibly missing) data involving two disjoint sets of individuals/objects corresponding to the rows/columns of the arrays. Typically, an...

The simultaneous clustering of observations and features of data sets (known as co-clustering) has recently emerged as a central machine learning application to summarize massive data sets. However, most existing models focus on continuous and dense data in stationary scenarios, where cluster assignments do not evolve over time. This work...

The simultaneous clustering of observations and features of data sets (a.k.a. co-clustering) has recently emerged as a central machine learning task to summarize massive data sets. However, most existing models focus on stationary scenarios, where cluster assignments do not evolve in time. This work introduces a novel latent block model for the...

The COVID-19 outbreak has stimulated the interest in the proposal of novel epidemiological models to predict the course of the epidemic so as to help planning effective control strategies. In particular, in order to properly interpret the available data, it has become clear that one must go beyond most classic epidemiological models and...

A lot of effort is currently made to provide methods to analyze and understand deep neural network impressive performances for tasks such as image or text classification. These methods are mainly based on visualizing the important input features taken into account by the network to build a decision. However these techniques, let us cite LIME,...

With the significant increase of interactions between individuals through numeric means, the clustering of vertex in graphs has become a fundamental approach for analysing large and complex networks. We propose here the deep latent position model (DeepLPM), an end-to-end clustering approach which combines the widely used latent position model...

We introduce a deep latent recommender system (deepLTRS) for imputing missing ratings based on the observed ratings and product reviews. Our approach extends a standard variational autoen-coder architecture associated with deep latent variable models in order to account for both the ordinal entries and the text entered by users to score and...

With the significant increase of interactions between individuals through numeric means, clustering of vertices in graphs has become a fundamental approach for analyzing large and complex networks. In this work, we propose the deep latent position model (DeepLPM), an end-to-end generative clustering approach which combines the widely used...

The simultaneous clustering of observations and features ofdatasets (known as co-clustering) has recently emerged as a central topic inmachine learning applications. However, most models focus on continuousdata in stationary scenarios, where cluster assignments do not evolve overtime. We propose in this paper the dynamic latent block model...

Most of existing graph neural networks (GNNs) developed for the prevalent text-rich networks typically treat texts as node attributes. This kind of approach unavoidably results in the loss of important semantic structures and restricts the representational power of GNNs. In this work, we introduce a document similarity-based graph convolutional...

In this paper, we consider textual interaction data involving two disjoint sets of individuals/objects. An example of such data is given by the reviews on web platforms (e.g. Amazon, TripAdvisor, etc.) where buyers comment on products/services they bought. We develop a new generative model, the latent topic block model (LTBM), along with an...

A lot of effort is currently made to provide methods to analyze and understand deep neuralnetwork impressive performances for tasks such as image or text classification. These methodsare mainly based on visualizing the important input features taken into account by the networkto build a decision. However these techniques, let us cite LIME,...

Pharmacovigilance is a central medical discipline aiming at monitoring and detecting public health events caused by medicines and vaccines. The purpose of this work is to analyze the notifications of adverse drug reactions (ADRs) gathered by the Regional Center of Pharmacovigilance of Nice (France) between 2010 to 2020. As the current expert...

Co-clustering is a widely used technique that allows the analysis of complex and high-dimensional data in various domains. However, existing models mostly concentrate on continuous and dense data in fixed time situations, where cluster assignments remain unchanged over time. For example, in the field of pharmacovigilance, it is crucial to...

The present paper develops a probabilistic model to cluster the nodes of a dynamic graph, accounting for the content of textual edges as well as their frequency. Ver-tices are clustered in groups which are homogeneous both in terms of interaction frequency and discussed topics. The dynamic graph is considered stationary on a latent time...

We introduce a deep latent recommender system named deepLTRS in order to provide users with high quality recommendations based on observed user ratings \textit{and} texts of product reviews. The underlying motivation is that, when a user scores only a few products, the texts used in the reviews represent a significant source of information,...

The taxonomic identification of charcoal is an inherent step in any anthracological analysis, but it can be challenging in some parts of the world with rich woody species. A combination of several anatomical criteria, or features, is necessary to describe a charcoal specimen. Regions with rich woody species are characterised by a high level of...

In this paper, we present the ZooKG-Pliny knowledge Graph constructed from a manual annotation of Pliny's Naturalis Historia using concepts gathered in the thesaurus TheZoo. ZooKG-Pliny is based on a semantic model that formalizes knowledge about the annotations of zoological information in texts. ZooKG-Pliny allows the integration and the...

This contribution compares statistical analysis and deep learning approaches to textual data. The extraction of "key passages" using statitics and deep learning is implemented using the Hyperbase sofware.

Since few years, some tools that are helping us to interpret results of deep learning have appeared (LIME, LSTMVIS, TDS). In this paper, we propose to go further by searching hidden information encoded in intermediate layers of deep learning thanks to a new tool. Hyperdeep allows, on the one hand, to predict the belonging of a text and to...