Sparse, hierarchical and shared-factors priors for representation learning

La représentation en caractéristiques est une préoccupation centrale des systèmes d’apprentissage automatique d’aujourd’hui. Une représentation adéquate peut faciliter une tâche d’apprentissage complexe. C’est le cas lorsque par exemple cette représentation est de faible dimensionnalité et est constituée de caractéristiques de haut niveau. Mais comment déterminer si une représentation est adéquate pour une tâche d’apprentissage ? Les récents travaux suggèrent qu’il est préférable de voir le choix de la représentation comme un problème d’apprentissage en soi. C’est ce que l’on nomme l’apprentissage de représentation. Cette thèse présente une série de contributions visant à améliorer la qualité des représentations apprises. La première contribution élabore une étude comparative des approches par dictionnaire parcimonieux sur le problème de la localisation de points de prises (pour la saisie robotisée) et fournit une analyse empirique de leurs avantages et leurs inconvénients. La deuxième contribution propose une architecture réseau de neurones à convolution (CNN) pour la détection de points de prise et la compare aux approches d’apprentissage par dictionnaire. Ensuite, la troisième contribution élabore une nouvelle fonction d’activation paramétrique et la valide expérimentalement. Finalement, la quatrième contribution détaille un nouveau mécanisme de partage souple de paramètres dans un cadre d’apprentissage multitâche.Feature representation is a central concern of today’s machine learning systems. A proper representation can facilitate a complex learning task. This is the case when for instance the representation has low dimensionality and consists of high-level characteristics. But how can we determine if a representation is adequate for a learning task? Recent work suggests that it is better to see the choice of representation as a learning problem in itself. This is called Representation Learning. This thesis presents a series of contributions aimed at improving the quality of the learned representations. The first contribution elaborates a comparative study of Sparse Dictionary Learning (SDL) approaches on the problem of grasp detection (for robotic grasping) and provides an empirical analysis of their advantages and disadvantages. The second contribution proposes a Convolutional Neural Network (CNN) architecture for grasp detection and compares it to SDL. Then, the third contribution elaborates a new parametric activation function and validates it experimentally. Finally, the fourth contribution details a new soft parameter sharing mechanism for multitasking learning

Color image-based shape reconstruction of multi-color objects under general illumination conditions

Humans have the ability to infer the surface reflectance properties and three-dimensional shape of objects from two-dimensional photographs under simple and complex illumination fields. Unfortunately, the reported algorithms in the area of shape reconstruction require a number of simplifying assumptions that result in poor performance in uncontrolled imaging environments. Of all these simplifications, the assumptions of non-constant surface reflectance, globally consistent illumination, and multiple surface views are the most likely to be contradicted in typical environments. In this dissertation, three automatic algorithms for the recovery of surface shape given non-constant reflectance using a single-color image acquired are presented. In addition, a novel method for the identification and removal of shadows from simple scenes is discussed.In existing shape reconstruction algorithms for surfaces of constant reflectance, constraints based on the assumed smoothness of the objects are not explicitly used. Through Explicit incorporation of surface smoothness properties, the algorithms presented in this work are able to overcome the limitations of the previously reported algorithms and accurately estimate shape in the presence of varying reflectance. The three techniques developed for recovering the shape of multi-color surfaces differ in the method through which they exploit the surface smoothness property. They are summarized below:• Surface Recovery using Pre-Segmentation - this algorithm pre-segments the image into distinct color regions and employs smoothness constraints at the color-change boundaries to constrain and recover surface shape. This technique is computationally efficient and works well for images with distinct color regions, but does not perform well in the presence of high-frequency color textures that are difficult to segment.iv• Surface Recovery via Normal Propagation - this approach utilizes local gradient information to propagate a smooth surface solution from points of known orientation. While solution propagation eliminates the need for color-based image segmentation, the quality of the recovered surface can be degraded by high degrees of image noise due to reliance on local information.• Surface Recovery by Global Variational Optimization - this algorithm utilizes a normal gradient smoothness constraint in a non-linear optimization strategy, to iteratively solve for the globally optimal object surface. Because of its global nature, this approach is much less sensitive to noise than the normal propagation is, but requires significantly more computational resources.Results acquired through application of the above algorithms to various synthetic and real image data sets are presented for qualitative evaluation. A quantitative analysis of the algorithms is also discussed for quadratic shapes. The robustness of the three approaches to factors such as segmentation error and random image noise is also explored

Spectral Study of Asteroids and Laboratory Simulation of Asteroid Organics

We investigate the spectra of asteroids at near- and mid-infrared wavelengths. In 2010 and 2011 we reported the detection of 3 ?m and 3.2-3.6 ?m signatures on (24) Themis and (65) Cybele indicative of water-ice and complex organics [1] [2] [3]. We further probed other primitive asteroids in the Cybele dynamical group and Themis family, finding diversity in the shape of their 3 ?m [4] [5] [6] and 10 ?m spectral features [4]. These differences indicated mineralogical and compositional variations within these asteroid populations. Also in the mid-infrared region we studied a larger population of asteroids belonging to the Bus C, D, and S taxanomic classes to understand the relationship between any mineralogy and hydration inferred in the visible and near- infrared with the shape, strength, and slope of the 10 ?m emission. We have discovered that at least 3 of the main Bus taxanomic groups (Cs, Ds, and Ss as defined by their visible spectra) clearly cluster into 3 statistically distinct groups based on their 8-13 ?m spectra. Additionally we have attempted to simulate in a laboratory the possible organic compounds we have detected on two asteroids, using various mixtures containing aromatic and aliphatic hydrocarbons. We find that asteroid (24) Themis and (65) Cybele have ?CH2/?CH3 and NCH2/NCH3 ratios similar to our 3- methylpentane, propane, and hexane residues, suggesting that the organics on these asteroids may be short chained and/or highly branched. The ?CH2/?CH3 and NCH2/NCH3 for asteroid(24)Themis are most consistent with the DISM, and some carbonaceous chondrites. The band centers of the C-H stretch absorptions indicate that both asteroids may have aliphatic carriers chemically bonded to electronegative groups (i.e. aromatics), and some that are not. We also detect a 3.45 ?m feature in the spectra of both asteroids that is present in several dense molecular clouds. Our results suggest an interstellar origin for the organics on (24) Themis, and likely (65) Cybele. The differences in the organics of Themis and Cybele are likely related to variations in thermal processing, irradiation and/or formation region in the solar nebula

A Geometric Approach for Deciphering Protein Structure from Cryo-EM Volumes

Electron Cryo-Microscopy or cryo-EM is an area that has received much attention in the recent past. Compared to the traditional methods of X-Ray Crystallography and NMR Spectroscopy, cryo-EM can be used to image much larger complexes, in many different conformations, and under a wide range of biochemical conditions. This is because it does not require the complex to be crystallisable. However, cryo-EM reconstructions are limited to intermediate resolutions, with the state-of-the-art being 3.6A, where secondary structure elements can be visually identified but not individual amino acid residues. This lack of atomic level resolution creates new computational challenges for protein structure identification. In this dissertation, we present a suite of geometric algorithms to address several aspects of protein modeling using cryo-EM density maps. Specifically, we develop novel methods to capture the shape of density volumes as geometric skeletons. We then use these skeletons to find secondary structure elements: SSEs) of a given protein, to identify the correspondence between these SSEs and those predicted from the primary sequence, and to register high-resolution protein structures onto the density volume. In addition, we designed and developed Gorgon, an interactive molecular modeling system, that integrates the above methods with other interactive routines to generate reliable and accurate protein backbone models

Methods for Generating High-Fidelity Trace Chemical Residue Reflectance Signatures for Active Spectroscopy Classification Applications

Standoff detection and identification of trace chemicals in hyperspectral infrared images is an enabling capability in a variety of applications relevant to defense, law enforcement, and intelligence communities. Performance of these methods is impacted by the spectral signature variability due to the presence of contaminants, surface roughness, nonlinear effects, etc. Though multiple classes of algorithms exist for the detection and classification of these signatures, they are limited by the availability of relevant reference datasets. In this work, we first address the lack of physics-based models that can accurately predict trace chemical spectra. Most available models assume that the chemical takes the form of spherical particles or uniform thin films. A more realistic chemical presentation that could be encountered is that of a non-uniform chemical film that is deposited after evaporation of the solvent which contained the chemical. This research presents an improved signature model for this type of solid film. The proposed model, called sparse transfer matrix (STM), includes a log-normal distribution of film thicknesses and is found to reduce the root-mean-square error between simulated and measured data by about 25% when compared with either the particle or uniform thin film models. When applied to measured data, the sparse transfer matrix model provides a 0.10-0.28 increase in classification accuracy over traditional models. There remain limitations in the STM model which prevent the predicted spectra from being well-matched to the measured data in some cases. To overcome this, we leverage the field of domain adaptation to translate data from the simulated to the measured data domain. This thesis presents the first one-dimensional (1D) conditional generative adversarial network (GAN) to perform spectrum-to-spectrum translation of reflectance signatures. We apply the 1D conditional GAN to a library of simulated spectra and quantify the improvement with the translated library. The method demonstrates an increase in overall classification accuracy to 0.723 from the accuracy of 0.622 achieved using the STM model when tested on real data. However, the performance improvement is biased towards data included in the GAN training set. The next phase of the research focuses on learning models that are more robust to different parameter combinations for which we do not have measured data. This part of the research leverages elements from the field of theory-guided data science. Specifically, we develop a physics-guided neural network (PGNN) for predicting chemical reflectance for a set of parameterized inputs that is more accurate than the state-of-the-art physics-based signature model for chemical residues. After training the PGNN, we use it to generate a library of predicted spectra for training a classifier. We compare the classification accuracy when using this PGNN library versus a library generated by the physics-based model. Using the PGNN, the average classification accuracy increases to 0.813 on real chemical reflectance data, including data from chemicals not included in the PGNN training set. The products of this thesis work include methods for producing realistic trace chemical residue reflectance signatures as well as demonstrations of improved performance in active spectroscopy classification applications. These methods provide great value to a range of scientific communities. The novel STM signature model enables existing spectroscopy sensors and algorithms to perform well on real-world problems where chemical contaminants are non-uniform. The 1D conditional GAN is the first of its kind and can be applied to many other 1D datasets, such as audio and other time-series data. Finally, the application of theory-guided data science to the trace chemical problem not only enhances the quality of results for known targets and backgrounds, but also increases the robustness to new targets