Advances in scaling deep learning algorithms

Les algorithmes d'apprentissage profond forment un nouvel ensemble de méthodes puissantes pour l'apprentissage automatique. L'idée est de combiner des couches de facteurs latents en hierarchies. Cela requiert souvent un coût computationel plus elevé et augmente aussi le nombre de paramètres du modèle. Ainsi, l'utilisation de ces méthodes sur des problèmes à plus grande échelle demande de réduire leur coût et aussi d'améliorer leur régularisation et leur optimization. Cette thèse adresse cette question sur ces trois perspectives. Nous étudions tout d'abord le problème de réduire le coût de certains algorithmes profonds. Nous proposons deux méthodes pour entrainer des machines de Boltzmann restreintes et des auto-encodeurs débruitants sur des distributions sparses à haute dimension. Ceci est important pour l'application de ces algorithmes pour le traitement de langues naturelles. Ces deux méthodes (Dauphin et al., 2011; Dauphin and Bengio, 2013) utilisent l'échantillonage par importance pour échantilloner l'objectif de ces modèles. Nous observons que cela réduit significativement le temps d'entrainement. L'accéleration atteint 2 ordres de magnitude sur plusieurs bancs d'essai. Deuxièmement, nous introduisont un puissant régularisateur pour les méthodes profondes. Les résultats expérimentaux démontrent qu'un bon régularisateur est crucial pour obtenir de bonnes performances avec des gros réseaux (Hinton et al., 2012). Dans Rifai et al. (2011), nous proposons un nouveau régularisateur qui combine l'apprentissage non-supervisé et la propagation de tangente (Simard et al., 1992). Cette méthode exploite des principes géometriques et permit au moment de la publication d'atteindre des résultats à l'état de l'art. Finalement, nous considérons le problème d'optimiser des surfaces non-convexes à haute dimensionalité comme celle des réseaux de neurones. Tradionellement, l'abondance de minimum locaux était considéré comme la principale difficulté dans ces problèmes. Dans Dauphin et al. (2014a) nous argumentons à partir de résultats en statistique physique, de la théorie des matrices aléatoires, de la théorie des réseaux de neurones et à partir de résultats expérimentaux qu'une difficulté plus profonde provient de la prolifération de points-selle. Dans ce papier nous proposons aussi une nouvelle méthode pour l'optimisation non-convexe.Deep learning algorithms are a new set of powerful methods for machine learning. The general idea is to combine layers of latent factors into hierarchies. This usually leads to a higher computational cost and having more parameters to tune. Thus scaling to larger problems will require not only reducing their computational cost but also improving regularization and optimization. This thesis investigates scaling from these three perspectives. We first study the problem of reducing the computational cost of some deep learning algorithms. We propose methods to scale restricted Boltzmann machines (RBM) and denoising auto-encoders (DAE) to very high-dimensional sparse distributions. This is important for applications of deep learning to natural language processing. Both methods (Dauphin et al., 2011; Dauphin and Bengio, 2013) rely on importance sampling to subsample the learning objective of these models. We show that this greatly reduces the training time, leading to 2 orders of magnitude speed ups on several benchmark datasets without losses in the quality of the model. Second, we introduce a powerful regularization method for deep neural nets. Experiments have shown that proper regularization is in many cases crucial to obtaining good performance out of larger networks (Hinton et al., 2012). In Rifai et al. (2011), we propose a new regularizer that combines unsupervised learning and tangent propagation (Simard et al., 1992). The method exploits several geometrical insights and was able at the time of publication to reach state-of-the-art results on competitive benchmarks. Finally, we consider the problem of optimizing over high-dimensional non-convex loss surfaces like those found in deep neural nets. Traditionally, the main difficulty in these problems is considered to be the abundance of local minima. In Dauphin et al. (2014a) we argue, based on results from statistical physics, random matrix theory, neural network theory, and empirical evidence, that the vast majority of critical points are saddle points, not local minima. We also propose a new optimization method for non-convex optimization

Doctor of Philosophy in Computing

dissertationAn important area of medical imaging research is studying anatomical diffeomorphic shape changes and detecting their relationship to disease processes. For example, neurodegenerative disorders change the shape of the brain, thus identifying differences between the healthy control subjects and patients affected by these diseases can help with understanding the disease processes. Previous research proposed a variety of mathematical approaches for statistical analysis of geometrical brain structure in three-dimensional (3D) medical imaging, including atlas building, brain variability quantification, regression, etc. The critical component in these statistical models is that the geometrical structure is represented by transformations rather than the actual image data. Despite the fact that such statistical models effectively provide a way for analyzing shape variation, none of them have a truly probabilistic interpretation. This dissertation contributes a novel Bayesian framework of statistical shape analysis for generic manifold data and its application to shape variability and brain magnetic resonance imaging (MRI). After we carefully define the distributions on manifolds, we then build Bayesian models for analyzing the intrinsic variability of manifold data, involving the mean point, principal modes, and parameter estimation. Because there is no closed-form solution for Bayesian inference of these models on manifolds, we develop a Markov Chain Monte Carlo method to sample the hidden variables from the distribution. The main advantages of these Bayesian approaches are that they provide parameter estimation and automatic dimensionality reduction for analyzing generic manifold-valued data, such as diffeomorphisms. Modeling the mean point of a group of images in a Bayesian manner allows for learning the regularity parameter from data directly rather than having to set it manually, which eliminates the effort of cross validation for parameter selection. In population studies, our Bayesian model of principal modes analysis (1) automatically extracts a low-dimensional, second-order statistics of manifold data variability and (2) gives a better geometric data fit than nonprobabilistic models. To make this Bayesian framework computationally more efficient for high-dimensional diffeomorphisms, this dissertation presents an algorithm, FLASH (finite-dimensional Lie algebras for shooting), that hugely speeds up the diffeomorphic image registration. Instead of formulating diffeomorphisms in a continuous variational problem, Flash defines a completely new discrete reparameterization of diffeomorphisms in a low-dimensional bandlimited velocity space, which results in the Bayesian inference via sampling on the space of diffeomorphisms being more feasible in time. Our entire Bayesian framework in this dissertation is used for statistical analysis of shape data and brain MRIs. It has the potential to improve hypothesis testing, classification, and mixture models

Algorithms for trajectory integration in multiple views

PhDThis thesis addresses the problem of deriving a coherent and accurate localization of moving objects from partial visual information when data are generated by cameras placed in di erent view angles with respect to the scene. The framework is built around applications of scene monitoring with multiple cameras. Firstly, we demonstrate how a geometric-based solution exploits the relationships between corresponding feature points across views and improves accuracy in object location. Then, we improve the estimation of objects location with geometric transformations that account for lens distortions. Additionally, we study the integration of the partial visual information generated by each individual sensor and their combination into one single frame of observation that considers object association and data fusion. Our approach is fully image-based, only relies on 2D constructs and does not require any complex computation in 3D space. We exploit the continuity and coherence in objects' motion when crossing cameras' elds of view. Additionally, we work under the assumption of planar ground plane and wide baseline (i.e. cameras' viewpoints are far apart). The main contributions are: i) the development of a framework for distributed visual sensing that accounts for inaccuracies in the geometry of multiple views; ii) the reduction of trajectory mapping errors using a statistical-based homography estimation; iii) the integration of a polynomial method for correcting inaccuracies caused by the cameras' lens distortion; iv) a global trajectory reconstruction algorithm that associates and integrates fragments of trajectories generated by each camera

Landing site reachability and decision making for UAS forced landings

After a huge amount of success within the military, the benefits of the use of unmanned aerial systems over manned aircraft is obvious. They are becoming cheaper and their functions advancing to such a point that there is now a large drive for their use by civilian operators. However there are a number of significant challenges that are slowing their inevitable integration into the national airspace systems of countries. A large array of emergency situations will need to be dealt with autonomously by contingency management systems to prevent potentially deadly incidences. One such emergency situation that will need autonomous intervention, is the total loss of thrust from engine failure. The complex multi faceted task of landing the stricken aircraft at a potentially unprepared site is called a forced landing. This thesis presents methods to address a number of critical parts of a forced landing system for use by an unmanned aerial system. In order for an emergency landing site to be considered, it needs to be within glide range. In order to find a landing site s reachability from the point of engine failure the aircraft s glide performance and a glide path must be known. A method by which to calculate the glide performance, both from aircraft parameters or experiments is shown. These are based on a number of steady state assumptions to make them generic and quick to compute. Despite the assumptions, these are shown to have reasonable accuracy. A minimum height loss path to the landing site is defined, which takes account of a steady uniform wind. While this path is not the path to be flown it enables a measure of how reachable a landing site is, as any extra height the aircraft has once it gets to the site makes a site more reachable. It is shown that this method is fast enough to be run online and is generic enough for use on a range of aircraft. Based on identified factors that make a landing site more suitable, a multi criteria decision making Bayesian network is developed to decide upon which site a unmanned aircraft should land in. It can handle uncertainty and non-complete information while guaranteeing a fast reasonable decision, which is critical in this time sensitive situation. A high fidelity simulation environment and flight test platform are developed in order to test the performance of the developed algorithms. The test environments developed enable rapid prototyping of algorithms not just within the scope of this thesis, but on a range of vehicle types. In simulation the minimum height loss paths show good accuracy, for two completely different types of aircraft. The decision making algorithms show that they are capable of being ran online in a flight test. They make a reasonable decision and are capable of quickly reacting to changing conditions, enabling redirection to a more suitable landing site

Statistical and Dynamical Modeling of Riemannian Trajectories with Application to Human Movement Analysis

abstract: The data explosion in the past decade is in part due to the widespread use of rich sensors that measure various physical phenomenon -- gyroscopes that measure orientation in phones and fitness devices, the Microsoft Kinect which measures depth information, etc. A typical application requires inferring the underlying physical phenomenon from data, which is done using machine learning. A fundamental assumption in training models is that the data is Euclidean, i.e. the metric is the standard Euclidean distance governed by the L-2 norm. However in many cases this assumption is violated, when the data lies on non Euclidean spaces such as Riemannian manifolds. While the underlying geometry accounts for the non-linearity, accurate analysis of human activity also requires temporal information to be taken into account. Human movement has a natural interpretation as a trajectory on the underlying feature manifold, as it evolves smoothly in time. A commonly occurring theme in many emerging problems is the need to \emph{represent, compare, and manipulate} such trajectories in a manner that respects the geometric constraints. This dissertation is a comprehensive treatise on modeling Riemannian trajectories to understand and exploit their statistical and dynamical properties. Such properties allow us to formulate novel representations for Riemannian trajectories. For example, the physical constraints on human movement are rarely considered, which results in an unnecessarily large space of features, making search, classification and other applications more complicated. Exploiting statistical properties can help us understand the \emph{true} space of such trajectories. In applications such as stroke rehabilitation where there is a need to differentiate between very similar kinds of movement, dynamical properties can be much more effective. In this regard, we propose a generalization to the Lyapunov exponent to Riemannian manifolds and show its effectiveness for human activity analysis. The theory developed in this thesis naturally leads to several benefits in areas such as data mining, compression, dimensionality reduction, classification, and regression.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Machine Learning for Informed Representation Learning

The way we view reality and reason about the processes surrounding us is intimately connected to our perception and the representations we form about our observations and experiences. The popularity of machine learning and deep learning techniques in that regard stems from their ability to form useful representations by learning from large sets of observations. Typical application examples include image recognition or language processing for which artificial neural networks are powerful tools to extract regularity patterns or relevant statistics. In this thesis, we leverage and further develop this representation learning capability to address relevant but challenging real-world problems in geoscience and chemistry, to learn representations in an informed manner relevant to the task at hand, and reason about representation learning in neural networks, in general. Firstly, we develop an approach for efficient and scalable semantic segmentation of degraded soil in alpine grasslands in remotely-sensed images based on convolutional neural networks. To this end, we consider different grassland erosion phenomena in several Swiss valleys. We find that we are able to monitor soil degradation consistent with state-of-the-art methods in geoscience and can improve detection of affected areas. Furthermore, our approach provides a scalable method for large-scale analysis which is infeasible with established methods. Secondly, we address the question of how to identify suitable latent representations to enable generation of novel objects with selected properties. For this, we introduce a new deep generative model in the context of manifold learning and disentanglement. Our model improves targeted generation of novel objects by making use of property cycle consistency in property-relevant and property-invariant latent subspaces. We demonstrate the improvements on the generation of molecules with desired physical or chemical properties. Furthermore, we show that our model facilitates interpretability and exploration of the latent representation. Thirdly, in the context of recent advances in deep learning theory and the neural tangent kernel, we empirically investigate the learning of feature representations in standard convolutional neural networks and corresponding random feature models given by the linearisation of the neural networks. We find that performance differences between standard and linearised networks generally increase with the difficulty of the task but decrease with the considered width or over-parametrisation of these networks. Our results indicate interesting implications for feature learning and random feature models as well as the generalisation performance of highly over-parametrised neural networks. In summary, we employ and study feature learning in neural networks and review how we may use informed representation learning for challenging tasks

TOWARD 3D RECONSTRUCTION OF STATIC AND DYNAMIC OBJECTS

The goal of image-based 3D reconstruction is to construct a spatial understanding of the world from a collection of images. For applications that seek to model generic real-world scenes, it is important that the reconstruction methods used are able to characterize both static scene elements (e.g. trees and buildings) as well as dynamic objects (e.g. cars and pedestrians). However, due to many inherent ambiguities in the reconstruction problem, recovering this 3D information with accuracy, robustness, and efficiency is a considerable challenge. To advance the research frontier for image-based 3D modeling, this dissertation focuses on three challenging problems in static scene and dynamic object reconstruction. We first target the problem of static scene depthmap estimation from crowd-sourced datasets (i.e. photos collected from the Internet). While achieving high-quality depthmaps using images taken under a controlled environment is already a difficult task, heterogeneous crowd-sourced data presents a unique set of challenges for multi-view depth estimation, including varying illumination and occasional occlusions. We propose a depthmap estimation method that demonstrates high accuracy, robustness, and scalability on a large number of photos collected from the Internet. Compared to static scene reconstruction, the problem of dynamic object reconstruction from monocular images is fundamentally ambiguous when not imposing any additional assumptions. This is because having only a single observation of an object is insufficient for valid 3D triangulation, which typically requires concurrent observations of the object from multiple viewpoints. Assuming that dynamic objects of the same class (e.g. all the pedestrians walking on a sidewalk) move in a common path in the real world, we develop a method that estimates the 3D positions of the dynamic objects from unstructured monocular images. Experiments on both synthetic and real datasets illustrate the solvability of the problem and the effectiveness of our approach. Finally, we address the problem of dynamic object reconstruction from a set of unsynchronized videos capturing the same dynamic event. This problem is of great interest because, due to the increased availability of portable capture devices, captures using multiple unsynchronized videos are common in the real world. To resolve the challenges that arises from non-concurrent captures and unknown temporal overlap among video streams, we propose a self-expressive dictionary learning framework, where the dictionary entries are defined as the collection of temporally varying structures. Experiments demonstrate the effectiveness of this approach to the previously unsolved problem.Doctor of Philosoph

Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021

As almost all human activities have been moved online due to the pandemic, novel robust and efficient approaches and further research have been in higher demand in the field of computer science and telecommunication. Therefore, this (reprint) book contains 13 high-quality papers presenting advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity

Knowledge-based systems and geological survey

This personal and pragmatic review of the philosophy underpinning methods of geological surveying suggests that important influences of information technology have yet to make their impact. Early approaches took existing systems as metaphors, retaining the separation of maps, map explanations and information archives, organised around map sheets of fixed boundaries, scale and content. But system design should look ahead: a computer-based knowledge system for the same purpose can be built around hierarchies of spatial objects and their relationships, with maps as one means of visualisation, and information types linked as hypermedia and integrated in mark-up languages. The system framework and ontology, derived from the general geoscience model, could support consistent representation of the underlying concepts and maintain reference information on object classes and their behaviour. Models of processes and historical configurations could clarify the reasoning at any level of object detail and introduce new concepts such as complex systems. The up-to-date interpretation might centre on spatial models, constructed with explicit geological reasoning and evaluation of uncertainties. Assuming (at a future time) full computer support, the field survey results could be collected in real time as a multimedia stream, hyperlinked to and interacting with the other parts of the system as appropriate. Throughout, the knowledge is seen as human knowledge, with interactive computer support for recording and storing the information and processing it by such means as interpolating, correlating, browsing, selecting, retrieving, manipulating, calculating, analysing, generalising, filtering, visualising and delivering the results. Responsibilities may have to be reconsidered for various aspects of the system, such as: field surveying; spatial models and interpretation; geological processes, past configurations and reasoning; standard setting, system framework and ontology maintenance; training; storage, preservation, and dissemination of digital records

Vision-based techniques for gait recognition

Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available - for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance