Learning Object Recognition and Object Class Segmentation with Deep Neural Networks on GPU

As cameras are becoming ubiquitous and internet storage abundant, the need for computers to understand images is growing rapidly. This thesis is concerned with two computer vision tasks, recognizing objects and their location, and segmenting images according to object classes. We focus on deep learning approaches, which in recent years had a tremendous influence on machine learning in general and computer vision in particular. The thesis presents our research into deep learning models and algorithms. It is divided into three parts. The first part describes our GPU deep learning framework. Its hierarchical structure allows transparent use of GPU, facilitates specification of complex models, model inspection, and constitutes the implementation basis of the later chapters. Components of this framework were used in a real-time GPU library for random forests, which we present and evaluate. In the second part, we investigate greedy learning techniques for semi-supervised object recognition. We improve the feature learning capabilities of restricted Boltzmann machines (RBM) with lateral interactions and auto-encoders with additional hidden layers, and offer empirical insight into the evaluation of RBM learning algorithms. The third part of this thesis focuses on object class segmentation. Here, we incrementally introduce novel neural network models and training algorithms, successively improving the state of the art on multiple datasets. Our novel methods include supervised pre-training, histogram of oriented gradient DNN inputs, depth normalization and recurrence. All contribute towards improving segmentation performance beyond what is possible with competitive baseline methods. We further demonstrate that pixelwise labeling combined with a structured loss function can be utilized to localize objects. Finally, we show how transfer learning in combination with object-centered depth colorization can be used to identify objects. We evaluate our proposed methods on the publicly available MNIST, MSRC, INRIA Graz-02, NYU-Depth, Pascal VOC, and Washington RGB-D Objects datasets.Allgegenwärtige Kameras und preiswerter Internetspeicher erzeugen einen großen Bedarf an Algorithmen für maschinelles Sehen. Die vorliegende Dissertation adressiert zwei Teilbereiche dieses Forschungsfeldes: Erkennung von Objekten und Objektklassensegmentierung. Der methodische Schwerpunkt liegt auf dem Lernen von tiefen Modellen (”Deep Learning“). Diese haben in den vergangenen Jahren einen enormen Einfluss auf maschinelles Lernen allgemein und speziell maschinelles Sehen gewonnen. Dabei behandeln wir behandeln wir drei Themenfelder. Der erste Teil der Arbeit beschreibt ein GPU-basiertes Softwaresystem für Deep Learning. Dessen hierarchische Struktur erlaubt schnelle GPU-Berechnungen, einfache Spezifikation komplexer Modelle und interaktive Modellanalyse. Damit liefert es das Fundament für die folgenden Kapitel. Teile des Systems finden Verwendung in einer Echtzeit-GPU-Bibliothek für Random Forests, die wir ebenfalls vorstellen und evaluieren. Der zweite Teil der Arbeit beleuchtet Greedy-Lernalgorithmen für halb überwachtes Lernen. Hier werden hierarchische Modelle schrittweise aus Modulen wie Autokodierern oder restricted Boltzmann Machines (RBM ) aufgebaut. Wir verbessern die Repräsentationsfähigkeiten von RBM auf Bildern durch Einführung lokaler und lateraler Verknüpfungen und liefern empirische Erkenntnisse zur Bewertung von RBM-Lernalgorithmen. Wir zeigen zudem, dass die in Autokodierern verwendeten einschichtigen Kodierer komplexe Zusammenhänge ihrer Eingaben nicht erkennen können und schlagen stattdessen einen hybriden Kodierer vor, der sowohl komplexe Zusammenhänge erkennen, als auch weiterhin einfache Zusammenhänge einfach repräsentieren kann. Im dritten Teil der Arbeit stellen wir neue neuronale Netzarchitekturen und Trainingsmethoden für die Objektklassensegmentierung vor. Wir zeigen, dass neuronale Netze mit überwachtem Vortrainieren, wiederverwendeten Ausgaben und Histogrammen Orientierter Gradienten (HOG) als Eingabe den aktuellen Stand der Technik auf mehreren RGB-Datenmengen erreichen können. Anschließend erweitern wir unsere Methoden in zwei Dimensionen, sodass sie mit Tiefendaten (RGB-D) und Videos verarbeiten können. Dazu führen wir zunächst Tiefennormalisierung für Objektklassensegmentierung ein um die Skala zu fixieren, und erlauben expliziten Zugriff auf die Höhe in einem Bildausschnitt. Schließlich stellen wir ein rekurrentes konvolutionales neuronales Netz vor, das einen großen räumlichen Kontext einbezieht, hochaufgelöste Ausgaben produziert und Videosequenzen verarbeiten kann. Dadurch verbessert sich die Bildsegmentierung relativ zu vergleichbaren Methoden, etwa auf der Basis von Random Forests oder CRF . Wir zeigen dann, dass pixelbasierte Ausgaben in neuronalen Netzen auch benutzt werden können um die Position von Objekten zu detektieren. Dazu kombinieren wir Techniken des strukturierten Lernens mit Konvolutionsnetzen. Schließlich schlagen wir eine objektzentrierte Einfärbungsmethode vor, die es ermöglicht auf RGB-Bildern trainierte neuronale Netze auf RGB-D-Bildern einzusetzen. Dieser Transferlernansatz erlaubt es uns auch mit stark reduzierten Trainingsmengen noch bessere Ergebnisse beim Schätzen von Objektklassen, -instanzen und -orientierungen zu erzielen. Wir werten die von uns vorgeschlagenen Methoden auf den öffentlich zugänglichen MNIST, MSRC, INRIA Graz-02, NYU-Depth, Pascal VOC, und Washington RGB-D Objects Datenmengen aus

Multi-Label Latent Spaces with Semi-Supervised Deep Generative Models

Expert labeling, tagging, and assessment are far more costly than the processes of collecting raw data. Generative modeling is a very powerful tool to tackle this real-world problem. It is shown here how these models can be used to allow for semi-supervised learning that performs very well in label-deficient conditions. The foundation for the work in this dissertation is built upon visualizing generative models\u27 latent spaces to gain deeper understanding of data, analyze faults, and propose solutions. A number of novel ideas and approaches are presented to improve single-label classification. This dissertation\u27s main focus is on extending semi-supervised Deep Generative Models for solving the multi-label problem by proposing unique mathematical and programming concepts and organization. In all naive mixtures, using multiple labels is detrimental and causes each label\u27s predictions to be worse than models that utilize only a single label. Examining latent spaces reveals that in many cases, large regions in the models generate meaningless results. Enforcing a priori independence is essential, and only when applied can multi-label models outperform the best single-label models. Finally, a novel learning technique called open-book learning is described that is capable of surpassing the state-of-the-art classification performance of generative models for multi-labeled, semi-supervised data sets

On Medical Image Segmentation and on Modeling Long Term Dependencies

La délimitation (segmentation) des tumeurs malignes à partir d’images médicales est importante pour le diagnostic du cancer, la planification des traitements ciblés, ainsi que les suivis de la progression du cancer et de la réponse aux traitements. Cependant, bien que la segmentation manuelle des images médicales soit précise, elle prend du temps, nécessite des opérateurs experts et est souvent peu pratique lorsque de grands ensembles de données sont utilisés. Ceci démontre la nécessité d’une segmentation automatique. Cependant, la segmentation automatisée des tumeurs est particulièrement difficile en raison de la variabilité de l’apparence des tumeurs, de l’équipement d’acquisition d’image et des paramètres d’acquisition, et de la variabilité entre les patients. Les tumeurs varient en type, taille, emplacement et quantité; le reste de l’image varie en raison des différences anatomiques entre les patients, d’une chirurgie antérieure ou d’une thérapie ablative, de différences dans l’amélioration du contraste des tissus et des artefacts d’image. De plus, les protocoles d’acquisition du scanner varient considérablement entre les cliniques et les caractéristiques de l’image varient selon le modèle du scanner. En raison de toutes ces variabilités, un modèle de segmentation doit être suffisamment flexible pour apprendre les caractéristiques générales des données. L’avènement des réseaux profonds de neurones à convolution (convolutional neural networks, CNN) a permis une classification exacte et précise des images hautement variables et, par extension, une segmentation de haute qualité des images. Cependant, ces modèles doivent être formés sur d’énormes quantités de données étiquetées. Cette contrainte est particulièrement difficile dans le contexte de la segmentation des images médicales, car le nombre de segmentations pouvant être produites est limité dans la pratique par la nécessité d’employer des opérateurs experts pour réaliser un tel étiquetage. De plus, les variabilités d’intérêt dans les images médicales semblent suivre une distribution à longue traîne, ce qui signifie qu’un nombre particulièrement important de données utilisées pour l’entraînement peut être nécessaire pour fournir un échantillon suffisant de chaque type de variabilité à un CNN. Cela démontre la nécessité de développer des stratégies pour la formation de ces modèles avec des segmentations de vérité-terrain disponibles limitées.----------ABSTRACT: The delineation (segmentation) of malignant tumours in medical images is important for cancer diagnosis, the planning of targeted treatments, and the tracking of cancer progression and treatment response. However, although manual segmentation of medical images is accurate, it is time consuming, requires expert operators, and is often impractical with large datasets. This motivates the need for training automated segmentation. However, automated segmentation of tumours is particularly challenging due to variability in tumour appearance, image acquisition equipment and acquisition parameters, and variability across patients. Tumours vary in type, size, location, and quantity; the rest of the image varies due to anatomical differences between patients, prior surgery or ablative therapy, differences in contrast enhancement of tissues, and image artefacts. Furthermore, scanner acquisition protocols vary considerably between clinical sites and image characteristics vary according to the scanner model. Due to all of these variabilities, a segmentation model must be flexible enough to learn general features from the data. The advent of deep convolutional neural networks (CNN) allowed for accurate and precise classification of highly variable images and, by extension, of high quality segmentation images. However, these models must be trained on enormous quantities of labeled data. This constraint is particularly challenging in the context of medical image segmentation because the number of segmentations that can be produced is limited in practice by the need to employ expert operators to do such labeling. Furthermore, the variabilities of interest in medical images appear to follow a long tail distribution, meaning a particularly large amount of training data may be required to provide a sufficient sample of each type of variability to a CNN. This motivates the need to develop strategies for training these models with limited ground truth segmentations available

Learning and time : on using memory and curricula for language understanding

Cette thèse présente quelques-unes des étapes entreprises pour pouvoir un jour résoudre le problème de la compréhension du langage naturel et d’apprentissage de dépendances à long terme, dans le but de développer de meilleurs algorithmes d’intelligence artificielle. Cette thèse est écrite comme une thèse par articles, et contient cinq publications scientifiques. Chacun de ces articles propose un nouveau modèle ou algorithme et démontre leur efficacité sur des problèmes qui impliquent des dépendances à long terme ou la compréhension du langage naturel. Malgré le fait que quelque uns de ces modèles n’ont été testés que sur une seule tâche (comme la traduction automatique neuronale), les méthodes proposées sont généralement applicables dans d’autres domaines et sur d’autres tâches. Dans l’introduction de la thèse, nous expliquons quelques concepts fondamentaux de l'entraînement de réseaux de neurones appliqués sur des données séquentielles. Tout d'abord, nous présentons succinctement les réseaux de neurones, puis, de façon plus détaillé, certains algorithmes et méthodes utilisés à travers cette thèse. Dans notre premier article, nous proposons une nouvelle méthode permettant d'utiliser la grande quantité de données monolingue disponible afin d'entraîner des modèles de traduction. Nous avons accompli cela en entraînant d’abord un modèle Long short-term memory (LSTM) sur un large corpus monolingue. Nous lions ensuite la sortie de la couche cachée du modèle avec celle d’un décodeur d’un modèle de traduction automatique. Ce dernier utilise un mécanisme d’attention et est entièrement entraîné par descente de gradient. Nous avons montré que la méthode proposée peut augmenter la performance des modèles de traduction automatique neuronale de façon significative sur les tâches où peu de données multilingues sont disponibles. Notre approche augmente également l’efficacité de l’utilisation des données dans les systèmes de traduction automatique. Nous montrons aussi des améliorations sur les paires de langues suivantes: turc-anglais, allemand-anglais, chinois-anglais et tchèque-anglais. Dans notre deuxième article, nous proposons une approche pour aborder le problème des mots rares dans plusieurs tâches du traitement des langages. Notre approche modifie l’architecture habituelle des modèles encodeur-décodeur avec attention, en remplaçant la couche softmax du décodeur par notre couche pointer-softmax. Celle-ci permet au décodeur de pointer à différents endroits dans la phrase d’origine. Notre modèle apprend à alterner entre copier un mot de la phrase d’origine et prédire un mot provenant d’une courte liste de mots prédéfinie, de manière probabiliste. L’approche que nous avons proposée est entièrement entraînable par descente de gradient et n’utilise qu’un objectif de maximum de vraisemblance sur les tâches de traduction. Nous avons aussi montré que le pointer-softmax aide de manière significative aux tâches de traduction et de synthèse de documents. Dans notre article "Plan, Attend, Generate: Planning for Sequence-to-Sequence Models", nous proposons deux approches pour apprendre l’alignement dans les modèles entraînés sur des séquences. Lorsque la longueur de l’entrée et celle de la sortie sont trop grandes, apprendre les alignements peut être très difficile. La raison est que lorsque le décodeur est trop puissant, il a tendance à ignorer l’alignement des mots pour ne se concentrer que sur le dernier mot de la séquence d’entrée. Nous avons proposé une nouvelle approche, inspirée d’un algorithme d’apprentissage par renforcement, en ajoutant explicitement un mécanisme de planification au décodeur. Ce nouveau mécanisme planifie à l’avance l’alignement pour les k prochaines prédictions. Notre modèle apprend également un plan de correction pour déterminer lorsqu’il est nécessaire de recalculer les alignements. Notre approche peut apprendre de haut niveaux d’abstraction au point de vue temporel et nous montrons que les alignements sont généralement de meilleure qualité. Nous obtenons également des gains de performance significatifs comparativement à notre modèle de référence, malgré le fait que nos modèles ont moins de paramètres et qu’ils aient été entraînés moins longtemps. Dans notre article "Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes", nous proposons une nouvelle approche pour ajouter de manière explicite un mécanisme de mémoire aux réseaux de neurones. Contrairement aux RNNs conventionnels, la mémoire n’est pas seulement représentée au niveau des activations du réseau, mais également dans une mémoire externe. Notre modèle, D-NTM, utilise un mécanisme d’adressage plus simple que les Neural Turing Machine (NTM) en utilisant des paires clé-valeur. Nous montrons que les modèles disposant de ce nouveau mécanisme peuvent plus efficacement apprendre les dépendances à long terme, en plus de mieux généraliser. Nous obtenons des améliorations sur plusieurs tâches incluant entre autres la réponse aux questions sur bAbI, le raisonnement avec implication, MNIST permuté, ainsi que des tâches synthétiques. Dans notre article "Noisy Activation Functions", nous proposons une nouvelle fonction d’activation, qui rend les activations stochastiques en leur ajoutant du bruit. Notre motivation dans cet article est d’aborder les problèmes d’optimisation qui surviennent lorsque nous utilisons des fonctions d’activation qui saturent, comme celles généralement utilisées dans les RNNs. Notre approche permet d’utiliser des fonctions d’activation linéaires par morceaux sur les RNNs à porte. Nous montrons des améliorations pour un grand nombre de tâches sans effectuer de recherche d'hyper paramètres intensive. Nous montrons également que supprimer le bruit dans les fonctions d’activation a un profond impact sur l’optimisation.The goal of this thesis is to present some of the small steps taken on the path towards solving natural language understanding and learning long-term dependencies to develop artificial intelligence algorithms that can reason with language. This thesis is written as a thesis by articles and contains five articles. Each article in this thesis proposes a new model or algorithm and demonstrates the efficiency of the proposed approach to solve problems that involve long-term dependencies or require natural language understanding. Although some of the models are tested on a particular task (such as neural machine translation), the proposed methods in this thesis are generally applicable to other domains and tasks (and have been used in the literature). In the introduction of this thesis, we introduce some of the fundamental concepts behind training sequence models using neural networks. We first provide a brief introduction to neural networks and then dive into details of the some of approaches and algorithms that are used throughout this thesis. In our first article, we propose a novel method to utilize the abundant amount of available monolingual data for training neural machine translation models. We have accomplished this goal by first training a long short-term memory (LSTM) language model on a large monolingual corpus and then fusing the outputs or the hidden states of the LSTM language model with the decoder of the neural machine translation model. Our neural machine translation model is trained end to end with an attention mechanism. We have shown that our proposed approaches can improve the performance of the neural machine translation models significantly on the rare resource translation tasks and our approach improved the data-efficiency of the end to end neural machine translation systems. We report improvements on Turkish-English (Tr-En), German-English (De-En), Chinese-English (Zh-En) and Czech-English (Cz-En) translation tasks. In our second paper, we propose an approach to address the problem of rare words for natural language processing tasks. Our approach augments the encoder-decoder architecture with attention model by replacing the final softmax layer with our proposed pointer-softmax layer that creates pointers to the source sentences as the decoder translates. In the case of pointer-softmax, our model learns to switch between copying a word from the source and predicting a word from a shortlist vocabulary in a probabilistic manner. Our proposed approach is end-to-end trainable with a single maximum likelihood objective of the NMT model. We have also shown that it improves the performance of summarization and the neural machine translation model. We report significant improvements in machine translation and summarization tasks. In our "Plan, Attend, Generate: Planning for Sequence-to-Sequence Models" paper, we propose two new approaches to learn alignments in a sequence to sequence model. If the input and the source context is very long, learning the alignments for a sequence to sequence model can be difficult. In particular, because when the decoder is a large network, it can learn to ignore the alignments and attend more on the last token of the input sequence. We propose a new approach which is inspired by a hierarchical reinforcement learning algorithm and extend our model with an explicit planning mechanism. The proposed alignment mechanism plans and computes the alignments for the next $k$ tokens in the decoder. Our model also learns a commitment plan to decide when to recompute the alignment matrix. Our proposed approach can learn high-level temporal abstractions, and we show that it qualitatively learns better alignments. We also achieve significant improvements over our baseline despite using smaller models and with less training. In "Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes," we propose a new approach for augmenting neural networks with an explicit memory mechanism. As opposed to conventional RNNs, the memory is not only represented in the activations of the neural network but in an external memory that can be accessed via the neural network controller. Our model, D-NTM uses a more straightforward memory addressing mechanism than NTM which is achieved by using key-value pairs for each memory cell. We find out that the models augmented with an external memory mechanism can learn tasks that involve long-term dependencies more efficiently and achieve better generalization. We achieve improvements on many tasks including but not limited to episodic question answering on bAbI, reasoning with entailment, permuted MNIST task and synthetic tasks. In our "Noisy Activation Functions" paper, we propose a novel activation function that makes the activations stochastic by injecting a particular form of noise to them. Our motivation in this paper is to address the optimization problem of commonly used saturating activation functions that are used with the recurrent neural networks. Our approach enables us to use piece-wise linear activation functions on the gated recurrent neural network models. We show improvements in a wide range of tasks without doing any extensive hyperparameter search by a drop-in replacement. We also show that annealing the noise of the activation function can have a profound continuation-like effect on the optimization of the network

Programming Languages and Systems

This open access book constitutes the proceedings of the 31st European Symposium on Programming, ESOP 2022, which was held during April 5-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 21 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems

Programming Languages and Systems

This open access book constitutes the proceedings of the 31st European Symposium on Programming, ESOP 2022, which was held during April 5-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 21 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems

Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

Approaching algorithmic power

Contemporary power manifests in the algorithmic. Emerging quite recently as an object of study within media and communications, cultural research, gender and race studies, and urban geography, the algorithm often seems ungraspable. Framed as code, it becomes proprietary property, black-boxed and inaccessible. Framed as a totality, its becomes overwhelmingly complex, incomprehensible in its operations. Framed as a procedure, it becomes a technique to be optimised, bracketing out the political. In struggling to adequately grasp the algorithmic as an object of study, to unravel its mechanisms and materialities, these framings offer limited insight into how algorithmic power is initiated and maintained. This thesis instead argues for an alternative approach: firstly, that the algorithmic is coordinated by a coherent internal logic, a knowledge-structure that understands the world in particular ways; second, that the algorithmic is enacted through control, a material and therefore observable performance which purposively influences people and things towards a predetermined outcome; and third, that this complex totality of architectures and operations can be productively analysed as strategic sociotechnical clusters of machines. This method of inquiry is developed with and tested against four contemporary examples: Uber, Airbnb, Amazon Alexa, and Palantir Gotham. Highly profitable, widely adopted and globally operational, they exemplify the algorithmic shift from whiteboard to world. But if the world is productive, it is also precarious, consisting of frictional spaces and antagonistic subjects. Force cannot be assumed as unilinear, but is incessantly negotiated—operations of parsing data and processing tasks forming broader operations that strive to establish subjectivities and shape relations. These negotiations can fail, destabilised by inadequate logics and weak control. A more generic understanding of logic and control enables a historiography of the algorithmic. The ability to index information, to structure the flow of labor, to exert force over subjects and spaces— these did not emerge with the microchip and the mainframe, but are part of a longer lineage of calculation. Two moments from this lineage are examined: house-numbering in the Habsburg Empire and punch-card machines in the Third Reich. Rather than revolutionary, this genealogy suggests an evolutionary process, albeit uneven, linking the computation of past and present. The thesis makes a methodological contribution to the nascent field of algorithmic studies. But more importantly, it renders algorithmic power more intelligible as a material force. Structured and implemented in particular ways, the design of logic and control construct different versions, or modalities, of algorithmic power. This power is political, it calibrates subjectivities towards certain ends, it prioritises space in specific ways, and it privileges particular practices whilst suppressing others. In apprehending operational logics, the practice of method thus foregrounds the sociopolitical dimensions of algorithmic power. As the algorithmic increasingly infiltrates into and governs the everyday, the ability to understand, critique, and intervene in this new field of power becomes more urgent

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 25th International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2022, which was held during April 4-6, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 23 regular papers presented in this volume were carefully reviewed and selected from 77 submissions. They deal with research on theories and methods to support the analysis, integration, synthesis, transformation, and verification of programs and software systems