The crucial need for reliable, robust and un-biased biodiversity data in support of initiatives such as the 30x30 initiative, which aims to conserve 30% of the world’s oceans by 2030, presents significant scientific and technological challenges. There have been advances made to automate fish biodiversity assessments using computer vision. However, the stark difference in research fields between ecology and artificial intelligence hinders the efficient use of computer vision tools for ecological tasks. This demo presents CleverFish, a novel tool designed to bridge the gap between artificial intelligence and marine biology. CleverFish tackles three core challenges of an efficient management tool: i) providing an easy-to-use graphical user interface to an AI pipeline, ii) accommodating global and video-specific in-app biodiversity assessment and iii) allowing fast and efficient extraction of temporal and spatial fish species distribution in a format understandable for ecologists. An accessible web application enables seamless integration into marine monitoring pipelines and conservation efforts.

Co-clustering is a technique used to analyze complex and high-dimensional data in various fields.However, traditional co-clustering methods are usually limited to dense data sets and require massive amount of memory, which can be limiting in some applications.To address this issue, we propose an online co-clustering model that processes the data incrementally and introduces a novel latent block model for sparse data matrices.The proposed model employs a LSTM neural network and a time and block dependent mixture of zero-inflated distributions to model sparsity and aims to detect real-time changes in dynamics through Bayesian online change point detection.An original variational procedure is proposed for inference.Simulations demonstrate the effectiveness of the methodology for count data.

Semi-supervised learning is a powerful technique for leveraging unlabeled data to improve machine learning models, but it can be affected by the presence of “informative" labels, which occur when some classes are more likely to be labeled than others. In the missing data literature, such labels are called missing not at random. In this paper, we propose a novel approach to address this issue by estimating the missing-data mechanism and using inverse propensity weighting to debias any SSL algorithm, including those using data augmentation. We also propose a likelihood ratio test to assess whether or not labels are indeed informative. Finally, we demonstrate the performance of the proposed methods on different datasets, in particular on two medical datasets for which we design pseudo-realistic missing data scenarios.

International audience

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 (LPM) for network analysis with a graph convolutional network (GCN) encoding strategy.Thus, DeepLPM can automatically assign each node to its group without using any additional algorithms and better preserves the network topology.Numerical experiments on simulated data and an application on the Cora citation network are conducted to demonstrate its effectiveness and interest in performing unsupervised clustering tasks.

In the last decade, recent successes in deep clustering majorly involved the mutual information (MI) as an unsupervised objective for training neural networks with increasing regularisations. While the quality of the regularisations have been largely discussed for improvements, little attention has been dedicated to the relevance of MI as a clustering objective. In this paper, we first highlight how the maximisation of MI does not lead to satisfying clusters. We identified the Kullback-Leibler divergence as the main reason of this behaviour. Hence, we generalise the mutual information by changing its core distance, introducing the generalised mutual information (GEMINI): a set of metrics for unsupervised neural network training. Unlike MI, some GEMINIs do not require regularisations when training. Some of these metrics are geometry-aware thanks to distances or kernels in the data space. Finally, we highlight that GEMINIs can automatically select a relevant number of clusters, a property that has been little studied in deep clustering context where the number of clusters is a priori unknown.

International audience

This work addresses the problem of patch-based image denoising through the unsupervised learning of a probabilistic high-dimensional mixture model on the noisy patches. The model, called HDMI, proposes a full modeling of the process that is supposed to have generated the noisy patches. To overcome the potential estimation problems due to the high dimension of the patches, the HDMI model adopts a parsimonious modeling which assumes that the data live in group-specific subspaces of low dimensionalities. This parsimonious modeling allows us in turn to get a numerically stable computation of the conditional expectation of the image which is applied for denoising. The use of such a model also permits us to rely on model selection tools, such as BIC, to automatically determine the intrinsic dimensions of the subspaces and the variance of the noise. This yields a denoising algorithm that can be used both when the noise level is known and is unknown.

With the flourishing development of high-dimensional data, sparse versions of principal component analysis (PCA) have imposed themselves as simple, yet powerful ways of selecting relevant features in an unsupervised manner. However, when several sparse principal components are computed, the interpretation of the selected variables may be difficult since each axis has its own sparsity pattern and has to be interpreted separately. To overcome this drawback, we propose a Bayesian procedure that allows to obtain several sparse components with the same sparsity pattern. To this end, using Roweis’ probabilistic interpretation of PCA and an isotropic Gaussian prior on the loading matrix, we provide the first exact computation of the marginal likelihood of a Bayesian PCA model. In order to avoid the drawbacks of discrete model selection, we propose a simple relaxation of our framework which allows to find a path of models using a variational expectation-maximization algorithm. The exact marginal likelihood can eventually be maximized over this path, relying on Occam’s razor to select the relevant variables. Since the sparsity pattern is common to all components, we call this approach globally sparse probabilistic PCA (GSPPCA). Its usefulness is illustrated on synthetic data sets and on several real unsupervised feature selection problems.

A family of parsimonious Gaussian process models for classification is proposed in this letter. A subspace assumption is used to build these models in the kernel feature space. By constraining some parameters of the models to be common between classes, parsimony is controlled. Experimental results are given for three real hyperspectral data sets, and comparisons are done with three other classifiers. The proposed models show good results in terms of classification accuracy and processing time.

A family of parsimonious Gaussian process models is presented. They allow to construct a Gaussian mixture model in a kernel feature space by assuming that the data of each class live in a specific subspace. The proposed models are used to build a kernel Markov random field (pGPMRF), which is applied to classify the pixels of a real multivariate remotely sensed image. In terms of classification accuracy, some of the proposed models perform equivalently to a SVM but they perform better than another kernel Gaussian mixture model previously defined in the literature. The pGPMRF provides the best classification accuracy thanks to the spatial regularization.

Graph-structured networks are often used to represent relationships between persons in organizations or communities. In this paper we investigate the problem of learning a latent space representation of the data in which proximity in the latent space increases the likelihood of a social tie between the nodes. In addition, this latent space representation can be used to classify these data into homogeneous groups in order to identify, for instance, marginal communities of persons. We propose a Bayesian way to select both dimension of the latent space and number of groups. We apply our approach to the Enron dataset and we show interesting representation and clustering of individuals.