Layer-wise learning of deep generative models

When using deep, multi-layered architectures to build generative models of data, it is difficult to train all layers at once. We propose a layer-wise training procedure admitting a performance guarantee compared to the global optimum. It is based on an optimistic proxy of future performance, the best latent marginal. We interpret auto-encoders in this setting as generative models, by showing that they train a lower bound of this criterion. We test the new learning procedure against a state of the art method (stacked RBMs), and find it to improve performance. Both theory and experiments highlight the importance, when training deep architectures, of using an inference model (from data to hidden variables) richer than the generative model (from hidden variables to data)

Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles

We present a canonical way to turn any smooth parametric family of probability distributions on an arbitrary search space $X$ into a continuous-time black-box optimization method on $X$, the \emph{information-geometric optimization} (IGO) method. Invariance as a design principle minimizes the number of arbitrary choices. The resulting \emph{IGO flow} conducts the natural gradient ascent of an adaptive, time-dependent, quantile-based transformation of the objective function. It makes no assumptions on the objective function to be optimized. The IGO method produces explicit IGO algorithms through time discretization. It naturally recovers versions of known algorithms and offers a systematic way to derive new ones. The cross-entropy method is recovered in a particular case, and can be extended into a smoothed, parametrization-independent maximum likelihood update (IGO-ML). For Gaussian distributions on $\mathbb{R}^d$, IGO is related to natural evolution strategies (NES) and recovers a version of the CMA-ES algorithm. For Bernoulli distributions on $\{0,1\}^d$, we recover the PBIL algorithm. From restricted Boltzmann machines, we obtain a novel algorithm for optimization on $\{0,1\}^d$. All these algorithms are unified under a single information-geometric optimization framework. Thanks to its intrinsic formulation, the IGO method achieves invariance under reparametrization of the search space $X$, under a change of parameters of the probability distributions, and under increasing transformations of the objective function. Theory strongly suggests that IGO algorithms have minimal loss in diversity during optimization, provided the initial diversity is high. First experiments using restricted Boltzmann machines confirm this insight. Thus IGO seems to provide, from information theory, an elegant way to spontaneously explore several valleys of a fitness landscape in a single run.Comment: Final published versio

Analysis of innate defences against Plasmodium falciparum in immunodeficient mice

Background: Mice with genetic deficiencies in adaptive immunity are used for the grafting of human cells or pathogens, to study human diseases, however, the innate immune responses to xenografts in these mice has received little attention. Using the NOD/SCID Plasmodium falciparum mouse model an analysis of innate defences responsible for the substantial control of P. falciparum which remains in such mice, was performed. Methods: NOD/SCID mice undergoing an immunomodulatory protocol that includes, clodronate-loaded liposomes to deplete macrophages and an anti-polymorphonuclear leukocytes antibody, were grafted with human red blood cells and P. falciparum. The systematic and kinetic analysis of the remaining innate immune responses included the number and phenotype of peripheral blood leukocytes as well as inflammatory cytokines/chemokines released in periphery. The innate responses towards the murine parasite Plasmodium yoelii were used as a control. Results: Results show that 1) P. falciparum induces a strong inflammation characterized by an increase in circulating leukocytes and the release of inflammatory cytokines; 2) in contrast, the rodent parasite P. yoelii, induces a far more moderate inflammation; 3) human red blood cells and the anti-inflammatory agents employed induce low-grade inflammation; and 4) macrophages seem to bear the most critical function in controlling P. falciparum survival in those mice, whereas polymorphonuclear and NK cells have only a minor role. Conclusions: Despite the use of an immunomodulatory treatment, immunodeficient NOD/SCID mice are still able to mount substantial innate responses that seem to be correlated with parasite clearance. Those results bring new insights on the ability of innate immunity from immunodeficient mice to control xenografts of cells of human origin and human pathogens

Sequential approaches for learning datum-wise sparse representations

International audienceIn supervised classification, data representation is usually considered at the dataset level: one looks for the "best" representation of data assuming it to be the same for all the data in the data space. We propose a different approach where the representations used for classification are tailored to each datum in the data space. One immediate goal is to obtain sparse datum-wise representations: our approach learns to build a representation specific to each datum that contains only a small subset of the features, thus allowing classification to be fast and efficient. This representation is obtained by way of a sequential decision process that sequentially chooses which features to acquire before classifying a particular point; this process is learned through algorithms based on Reinforcement Learning. The proposed method performs well on an ensemble of medium-sized sparse classification problems. It offers an alternative to global sparsity approaches, and is a natural framework for sequential classification problems. The method extends easily to a whole family of sparsity-related problem which would otherwise require developing specific solutions. This is the case in particular for cost-sensitive and limited-budget classification, where feature acquisition is costly and is often performed sequentially. Finally, our approach can handle non-differentiable loss functions or combinatorial optimization encountered in more complex feature selection problems

Apprentissage par renforcement rapide pour des grands ensembles d'actions en utilisant des codes correcteurs d'erreur

National audienceL'utilisation de l'apprentissage par renforcement (AR) pour la résolution de problèmes réalistes se heurte à des questions de passage à l'échelle. La plupart des algorithmes d'AR sont incapables de gérer des problèmes avec des centaines, voire des milliers d'actions, ce qui en limite l'application dans la pratique. Nous considérons le problème d'AR dans le cadre de l'apprentissage supervisé où la politique optimale est obtenue sous la forme d'un classeur multi-classes, l'ensemble des classes correspondant à l'ensemble des actions du problème. Nous introduisons l'utilisation de codes correcteurs d'erreurs (CCE) dans ce contexte et proposons deux nouvelles méthodes pour réduire la complexité de l'apprentissage en utilisant des approches à base de rollouts. La première de ces méthodes consiste à introduire un classeur basé sur des CCE comme classeur multi-classes, ce qui réduit la complexité de l'apprentissage de O(A^2 ) a O(A log(A)). Ensuite, nous proposons une seconde méthode qui met à profit le dictionnaire de codage du CCE pour découper le PDM initial en O(log(A)) PDM à 2 actions. Cette seconde méthode réduit la complexité de l'apprentissage de O(A^2) a O(log(A)) ce qui permet de traiter en des temps très raisonnables des problèmes avec un grand nombre d'actions. Nous terminons avec une démonstration expérimentale de l'intérêt de notre approche

Further Improvements of the P. falciparum Humanized Mouse Model

BACKGROUND: It has been shown previously that it is possible to obtain growth of Plasmodium falciparum in human erythrocytes grafted in mice lacking adaptive immune responses by controlling, to a certain extent, innate defences with liposomes containing clodronate (clo-lip). However, the reproducibility of those models is limited, with only a proportion of animals supporting longstanding parasitemia, due to strong inflammation induced by P. falciparum. Optimisation of the model is much needed for the study of new anti-malarial drugs, drug combinations, and candidate vaccines. MATERIALS/METHODS: We investigated the possibility of improving previous models by employing the intravenous route (IV) for delivery of both human erythrocytes (huRBC) and P. falciparum, instead of the intraperitoneal route (IP), by testing various immunosuppressive drugs that might help to control innate mouse defences, and by exploring the potential benefits of using immunodeficient mice with additional genetic defects, such as those with IL-2Rγ deficiency (NSG mice). RESULTS: We demonstrate here the role of aging, of inosine and of the IL-2 receptor γ mutation in controlling P. falciparum induced inflammation. IV delivery of huRBC and P. falciparum in clo-lip treated NSG mice led to successful infection in 100% of inoculated mice, rapid rise of parasitemia to high levels (up to 40%), long-lasting parasitemia, and consistent results from mouse-to-mouse. Characteristics were closer to human infection than in previous models, with evidence of synchronisation, partial sequestration, and receptivity to various P. falciparum strains without preliminary adaptation. However, results show that a major IL-12p70 inflammatory response remains prevalent. CONCLUSION: The combination of the NSG mouse, clodronate loaded liposomes, and IV delivery of huRBC has produced a reliable and more relevant model that better meets the needs of Malaria research

Datum-wise classification. A sequential Approach to sparsity

International audienceWe propose a novel classification technique whose aim is to select an appropriate representation for each datapoint, in contrast to the usual approach of selecting a representation encompassing the whole dataset. This datum-wise representation is found by using a sparsity inducing empirical risk, which is a relaxation of the standard L 0 regularized risk. The classification problem is modeled as a sequential decision process that sequentially chooses, for each datapoint, which features to use before classifying. Datum-Wise Classification extends naturally to multi-class tasks, and we describe a specific case where our inference has equivalent complexity to a traditional linear classifier, while still using a variable number of features. We compare our classifier to classical L 1 regularized linear models (L 1-SVM and LARS) on a set of common binary and multi-class datasets and show that for an equal average number of features used we can get improved performance using our method

Oxo-Functionalization and Reduction of the Uranyl Ion through Lanthanide-Element Bond Homolysis:Synthetic, Structural, and Bonding Analysis of a Series of Singly Reduced Uranyl-Rare Earth 5f¹-4f^<em>n</em> Complexes

The heterobimetallic complexes [{UO2Ln-(py)2(L)}2], combining a singly reduced uranyl cation and a rare-earth trication in a binucleating polypyrrole Schiff-base macrocycle (Pacman) and bridged through a uranyl oxo-group, have been prepared for Ln = Sc, Y, Ce, Sm, Eu, Gd, Dy, Er, Yb, and Lu. These compounds are formed by the single-electron reduction of the Pacman uranyl complex [UO2(py)(H2L)] by the rare-earth complexes LnIII(A)3 (A = N(SiMe3)2, OC6H3But 2-2,6) via homolysis of a Ln−A bond. The complexes are dimeric through mutual uranyl exo-oxo coordination but can be cleaved to form the trimetallic, monouranyl “ate” complexes [(py)3LiOUO(μ-X)Ln(py)(L)] by the addition of lithium halides. X-ray crystallographic structural characterization of many examples reveals very similar features for monomeric and dimeric series, the dimers containing an asymmetric U2O2 diamond core with shorter uranyl U=O distances than in the monomeric complexes. The synthesis by LnIII−A homolysis allows [5f1-4fn]2 and Li[5f1-4fn] complexes with oxobridged metal cations to be made for all possible 4fn configurations. Variable-temperature SQUID magnetometry and IR, NIR, and EPR spectroscopies on the complexes are utilized to provide a basis for the better understanding of the electronic structure of f-block complexes and their f-electron exchange interactions. Furthermore, the structures, calculated by restricted-core or allelectron methods, are compared along with the proposed mechanism of formation of the complexes. A strong antiferromagnetic coupling between the metal centers, mediated by the oxo groups, exists in the UVSmIII monomer, whereas the dimeric UVDyIII complex was found to show magnetic bistability at 3 K, a property required for the development of single-molecule magnets.JRC.E.6-Actinide researc