Preference-based Representation Learning for Collections

In this thesis, I make some contributions to the development of representation learning in the setting of external constraints and noisy supervision. A setting of external constraints refers to the scenario in which the learner is forced to output a latent representation of the given data points while enforcing some particular conditions. These conditions can be geometrical constraints, for example forcing the vector embeddings to be close to each other based on a particular relations, or forcing the embedding vectors to lie in a particular manifold, such as the manifold of vectors whose elements sum to 1, or even more complex constraints. The objects of interest in this thesis are elements of a collection X in an abstract space that is endowed with a similarity function which quantifies how similar two objects are. A collection is defined as a set of items in which the order is ignored but the multiplicity is relevant. Various types of collections are used as inputs or outputs in the machine learning field. The most common are perhaps sequences and sets. Besides studying representation learning approaches in presence of external constraints, in this thesis we tackle the case in which the evaluation of this similarity function is not directly possible. In recent years, the machine learning setting of having only binary answers to some comparisons for tuples of elements has gained interest. Learning good representations from a scenario in which a clear distance information cannot be obtained is of fundamental importance. This problem is opposite to the standard machine learning setting where the similarity function between elements can be directly evaluated. Moreover, we tackle the case in which the learner is given noisy supervision signals, with a certain probability for the label to be incorrect. Another research question that was studied in this thesis is how to assess the quality of the learned representations and how a learner can convey the uncertainty about this representation. After the introductory Chapter 1, the thesis is structured in three main parts. In the first part, I present the results of representation learning based on data points that are sequences. The focus in this part is on sentences and permutations, particular types of sequences. The first contribution of this part consists in enforcing analogical relations between sentences and the second is learning appropriate representations for permutations, which are particular mathematical objects, while using neural networks. The second part of this thesis tackles the question of learning perceptual embeddings from binary and noisy comparisons. In machine learning, this problem is referred as ordinal embedding problem. This part contains two chapters which elaborate two different aspects of the problem: appropriately conveying the uncertainty of the representation and learning the embeddings from aggregated and noisy feedback. Finally the third part of the thesis, contains applications of the findings of the previous part, namely unsupervised alignment of clouds of embedding vectors and entity set extension

Surface analysis and visualization from multi-light image collections

Multi-Light Image Collections (MLICs) are stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination that provides large amounts of visual and geometric information. Over the last decades, a wide variety of methods have been devised to extract information from MLICs and have shown its use in different application domains to support daily activities. In this thesis, we present methods that leverage a MLICs for surface analysis and visualization. First, we provide background information: acquisition setup, light calibration and application areas where MLICs have been successfully used for the research of daily analysis work. Following, we discuss the use of MLIC for surface visualization and analysis and available tools used to support the analysis. Here, we discuss methods that strive to support the direct exploration of the captured MLIC, methods that generate relightable models from MLIC, non-photorealistic visualization methods that rely on MLIC, methods that estimate normal map from MLIC and we point out visualization tools used to do MLIC analysis. In chapter 3 we propose novel benchmark datasets (RealRTI, SynthRTI and SynthPS) that can be used to evaluate algorithms that rely on MLIC and discusses available benchmark for validation of photometric algorithms that can be also used to validate other MLIC-based algorithms. In chapter 4, we evaluate the performance of different photometric stereo algorithms using SynthPS for cultural heritage applications. RealRTI and SynthRTI have been used to evaluate the performance of (Neural)RTI method. Then, in chapter 5, we present a neural network-based RTI method, aka NeuralRTI, a framework for pixel-based encoding and relighting of RTI data. In this method using a simple autoencoder architecture, we show that it is possible to obtain a highly compressed representation that better preserves the original information and provides increased quality of virtual images relighted from novel directions, particularly in the case of challenging glossy materials. Finally, in chapter 6, we present a method for the detection of crack on the surface of paintings from multi-light image acquisitions and that can be used as well on single images and conclude our presentation

Hyperbolic Deep Neural Networks: A Survey

Recently, there has been a rising surge of momentum for deep representation learning in hyperbolic spaces due to theirhigh capacity of modeling data like knowledge graphs or synonym hierarchies, possessing hierarchical structure. We refer to the model as hyperbolic deep neural network in this paper. Such a hyperbolic neural architecture potentially leads to drastically compact model withmuch more physical interpretability than its counterpart in Euclidean space. To stimulate future research, this paper presents acoherent and comprehensive review of the literature around the neural components in the construction of hyperbolic deep neuralnetworks, as well as the generalization of the leading deep approaches to the Hyperbolic space. It also presents current applicationsaround various machine learning tasks on several publicly available datasets, together with insightful observations and identifying openquestions and promising future directions

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202

Learning by correlation for computer vision applications: from Kernel methods to deep learning

Learning to spot analogies and differences within/across visual categories is an arguably powerful approach in machine learning and pattern recognition which is directly inspired by human cognition. In this thesis, we investigate a variety of approaches which are primarily driven by correlation and tackle several computer vision applications

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways