Feedforward deep architectures for classification and synthesis

Cette thèse par article présente plusieurs contributions au domaine de l'apprentissage de représentations profondes, avec des applications aux problèmes de classification et de synthèse d'images naturelles. Plus spécifiquement, cette thèse présente plusieurs nouvelles techniques pour la construction et l'entraînment de réseaux neuronaux profonds, ainsi q'une étude empirique de la technique de «dropout», une des approches de régularisation les plus populaires des dernières années. Le premier article présente une nouvelle fonction d'activation linéaire par morceau, appellée «maxout», qui permet à chaque unité cachée d'un réseau de neurones d'apprendre sa propre fonction d'activation convexe. Nous démontrons une performance améliorée sur plusieurs tâches d'évaluation du domaine de reconnaissance d'objets, et nous examinons empiriquement les sources de cette amélioration, y compris une meilleure synergie avec la méthode de régularisation «dropout» récemment proposée. Le second article poursuit l'examen de la technique «dropout». Nous nous concentrons sur les réseaux avec fonctions d'activation rectifiées linéaires (ReLU) et répondons empiriquement à plusieurs questions concernant l'efficacité remarquable de «dropout» en tant que régularisateur, incluant les questions portant sur la méthode rapide de rééchelonnement au temps de l´évaluation et la moyenne géometrique que cette méthode approxime, l'interprétation d'ensemble comparée aux ensembles traditionnels, et l'importance d'employer des critères similaires au «bagging» pour l'optimisation. Le troisième article s'intéresse à un problème pratique de l'application à l'échelle industrielle de réseaux neuronaux profonds au problème de reconnaissance d'objets avec plusieurs etiquettes, nommément l'amélioration de la capacité d'un modèle à discriminer entre des étiquettes fréquemment confondues. Nous résolvons le problème en employant la prédiction du réseau des sous-composantes dédiées à chaque sous-ensemble de la partition. Finalement, le quatrième article s'attaque au problème de l'entraînment de modèles génératifs adversariaux (GAN) récemment proposé. Nous présentons une procédure d'entraînment améliorée employant un auto-encodeur débruitant, entraîné dans un espace caractéristiques abstrait appris par le discriminateur, pour guider le générateur à apprendre un encodage qui s'aligne de plus près aux données. Nous évaluons le modèle avec le score «Inception» récemment proposé.This thesis by articles makes several contributions to the field of deep learning, with applications to both classification and synthesis of natural images. Specifically, we introduce several new techniques for the construction and training of deep feedforward networks, and present an empirical investigation into dropout, one of the most popular regularization strategies of the last several years. In the first article, we present a novel piece-wise linear parameterization of neural networks, maxout, which allows each hidden unit of a neural network to effectively learn its own convex activation function. We demonstrate improvements on several object recognition benchmarks, and empirically investigate the source of these improvements, including an improved synergy with the recently proposed dropout regularization method. In the second article, we further interrogate the dropout algorithm in particular. Focusing on networks of the popular rectified linear units (ReLU), we empirically examine several questions regarding dropout’s remarkable effectiveness as a regularizer, including questions surrounding the fast test-time rescaling trick and the geometric mean it approximates, interpretations as an ensemble as compared with traditional ensembles, and the importance of using a bagging-like criterion for optimization. In the third article, we address a practical problem in industrial-scale application of deep networks for multi-label object recognition, namely improving an existing model’s ability to discriminate between frequently confused classes. We accomplish this by using the network’s own predictions to inform a partitioning of the label space, and augment the network with dedicated discriminative capacity addressing each of the partitions. Finally, in the fourth article, we tackle the problem of fitting implicit generative models of open domain collections of natural images using the recently introduced Generative Adversarial Networks (GAN) paradigm. We introduce an augmented training procedure which employs a denoising autoencoder, trained in a high-level feature space learned by the discriminator, to guide the generator towards feature encodings which more closely resemble the data. We quantitatively evaluate our findings using the recently proposed Inception score

GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function

Abstract Background: Most successful computational approaches for protein function prediction integrate multiple genomics and proteomics data sources to make inferences about the function of unknown proteins. The most accurate of these algorithms have long running times, making them unsuitable for real-time protein function prediction in large genomes. As a result, the predictions of these algorithms are stored in static databases that can easily become outdated. We propose a new algorithm, GeneMANIA, that is as accurate as the leading methods, while capable of predicting protein function in real-time. Results: We use a fast heuristic algorithm, derived from ridge regression, to integrate multiple functional association networks and predict gene function from a single process-specific network using label propagation. Our algorithm is efficient enough to be deployed on a modern webserver and is as accurate as, or more so than, the leading methods on the MouseFunc I benchmark and a new yeast function prediction benchmark; it is robust to redundant and irrelevant data and requires, on average, less than ten seconds of computation time on tasks from these benchmarks. Conclusion: GeneMANIA is fast enough to predict gene function on-the-fly while achieving state-of-the-art accuracy. A prototype version of a GeneMANIA-based webserver is available at http://morrislab.med.utoronto.ca/prototype

Theano: new features and speed improvements

Theano is a linear algebra compiler that optimizes a user's symbolically-specified mathematical computations to produce efficient low-level implementations. In this paper, we present new features and efficiency improvements to Theano, and benchmarks demonstrating Theano's performance relative to Torch7, a recently introduced machine learning library, and to RNNLM, a C++ library targeted at recurrent neural networks.Comment: Presented at the Deep Learning Workshop, NIPS 201

Brain Tumor Segmentation with Deep Neural Networks

In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test dataset reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster