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Дисертації з теми "Réseaux neuronaux graphiques"
Lachaud, Guillaume. "Extensions and Applications of Graph Neural Networks." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS434.
Повний текст джерелаGraphs are used everywhere to represent interactions between entities, whether physical such as atoms, molecules or people, or more abstract such as cities, friendships, ideas, etc. Amongst all the methods of machine learning that can be used, the recent advances in deep learning have made graph neural networks the de facto standard for graph representation learning. This thesis can be divided in two parts. First, we review the theoretical underpinnings of the most powerful graph neural networks. Second, we explore the challenges faced by the existing models when training on real world graph data. The powerfulness of a graph neural network is defined in terms of its expressiveness, i.e., its ability to distinguish non isomorphic graphs; or, in an equivalent manner, its ability to approximate permutation invariant and equivariant functions. We distinguish two broad families of the most powerful models. We summarise the mathematical properties as well as the advantages and disadvantages of these models in practical situations. Apart from the choice of the architecture, the quality of the graph data plays a crucial role in the ability to learn useful representations. Several challenges are faced by graph neural networks given the intrinsic nature of graph data. In contrast to typical machine learning methods that deal with tabular data, graph neural networks need to consider not only the features of the nodes but also the interconnectedness between them. Due to the connections between nodes, training neural networks on graphs can be done in two settings: in transductive learning, the model can have access to the test features in the training phase; in the inductive setting, the test data remains unseen. We study the differences in terms of performance between inductive and transductive learning for the node classification task. Additionally, the features that are fed to a model can be noisy or even missing. In this thesis we evaluate these challenges on real world datasets, and we propose a novel architecture to perform missing data imputation on graphs. Finally, while graphs can be the natural way to describe interactions, other types of data can benefit from being converted into graphs. In this thesis, we perform preliminary work on how to extract the most important parts of skin lesion images that could be used to create graphs and learn hidden relations in the data
Badr, Bellaj. "Securing P2P resource sharing via blockchain and GNN-based trust." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS005.
Повний текст джерелаThe emergence of blockchain technology and cryptocurrencies has enabled the development of innovative peer-to-peer (P2P) models for resource allocation, sharing, and monetization. As these P2P models operate without inherent trust, the need for reliable trust and reputation mechanisms becomes crucial to minimize potential risks associated with engaging with malicious peers. Several trust management systems (TMS) have been proposed to establish trust in traditional P2P networks, aiming to facilitate the selection of dependable resources and deter peer misbehavior, with a significant focus on utilizing reputation as a guiding factor.Reputation-based trust systems (RTMS) play a fundamental role by leveraging community-based reputations to establish trust. They enable peers to assess the trustworthiness of others and evaluate the Quality of Service (QoS) based on shared reputations and past interactions. While these systems establish a peer-to-peer overlay trust network, the majority of these protocols are not tailored to suit Blockchain-based networks, resulting in various shortcomings due to their outdated design.This thesis presents our protocol BTrust, a novel decentralized and modular trust management system for large-scale P2P networks, leveraging blockchain technology and (Graph Neural Network) GNN for trust evaluation. BTrust introduces a multi-dimensional trust and reputation model to assess peer trustworthiness, dynamically deriving a single value from multiple parameters. The blockchain ensures reliable trust computation, dissemination, and storage without a central trust manager.An important breakthrough in our protocol is the resolution of the "cold start" or "initial trust score problem". To achieve this, the bootstrapping peer adopts random walks to select trustworthy peers among its neighbors, ensuring a decentralized approach without relying on any centralized entity or predefined peers. Unlike existing solutions, this method prevents overwhelming the most trusted peers in the network.Another challenge addressed in reputation systems is the reluctance of peers to provide negative feedback, often due to fear of retaliation or simply not providing feedback at all. To tackle these issues, we introduce an incentive mechanism that encourages truthful feedback and implement specialized mechanisms to penalize bad or lazy behavior. These innovations promote a more reliable and balanced trust evaluation process within the system.Furthermore, we propose a variant of BTrust called GBTrust, which improves upon the original protocol by incorporating Graph Neural Networks (GNNs) and a novel attention-based mechanism specifically designed for trust management. This variant enhances the detection of dynamic malicious peers and strengthens the overall robustness and accuracy of trust evaluation. By leveraging GNNs, GBTrust effectively captures the complex relationships and dynamic behavior of peers in the network, enabling more accurate identification of malicious activities and better adaptability to changing trust dynamics. The attention-based mechanism further enhances the model's ability to prioritize and weigh different trust factors, leading to more reliable and precise trust assessments.We demonstrate the efficiency of the proposed protocol in large-scale P2P networks using simulations of a P2P network and show that BTrust and its variant (GBTrust) are highly resilient to failures and robust against malicious nodes
Hafidi, Hakim. "Robust machine learning for Graphs/Networks." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT004.
Повний текст джерелаThis thesis addresses advancements in graph representation learning, focusing on the challengesand opportunities presented by Graph Neural Networks (GNNs). It highlights the significanceof graphs in representing complex systems and the necessity of learning node embeddings that capture both node features and graph structure. The study identifies key issues in GNNs, such as their dependence on high-quality labeled data, inconsistent performanceacross various datasets, and susceptibility to adversarial attacks.To tackle these challenges, the thesis introduces several innovative approaches. Firstly, it employs contrastive learning for node representation, enabling self-supervised learning that reduces reliance on labeled data. Secondly, a Bayesian-based classifier isproposed for node classification, which considers the graph’s structure to enhance accuracy. Lastly, the thesis addresses the vulnerability of GNNs to adversarialattacks by assessing the robustness of the proposed classifier and introducing effective defense mechanisms.These contributions aim to improve both the performance and resilience of GNNs in graph representation learning
Akakzia, Ahmed. "Teaching Predicate-based Autotelic Agents." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS415.pdf.
Повний текст джерелаAs part of the quest for designing embodied machines that autonomously explore their environments, discover new behaviors and acquire open-ended repertoire of skills, artificial intelligence has been taking long looks at the inspiring fields of developmental psychology and cognitive sciences which investigate the remarkable continuous and unbounded learning of humans. This gave birth to the field of developmental robotics which aims at designing autonomous artificial agents capable of self-organizing their own learning trajectories based on their intrinsic motivations. It bakes the developmental framework of intrinsically motivated goal exploration processes (IMGEPs) into reinforcement learning (RL). This combination has been recently introduced as autotelic reinforcement learning, where autotelic agents are intrinsically motivated to self-represent, self-organize and autonomously learn about their own goals. Naturally, such agents need to be endowed with good exploration capabilities as they need to first physically encounter a certain goal in order to take ownership of and learn about it. Unfortunately, discovering interesting behavior is usually tricky, especially in hard exploration setups where the rewarding signals are parsimonious, deceptive or adversarial. In such scenarios, the agents’ physical situatedness-in the Piagetian sense of the term-seems insufficient. Luckily, research in developmental psychology and education sciences have been praising the remarkable role of socio-cultural signals in the development of human children. This social situatedness-in the Vygotskyan sense of the term-enhances the toddlers’ exploration capabilities, creativity and development. However, deep \rl considers social interactions as dictating instructions to the agents, depriving them from their autonomy. This research introduces \textit{teachable autotelic agents}, a novel family of autonomous machines that can learn both alone and from external social signals. We formalize such a family as a hybrid goal exploration process (HGEPs), where autotelic agents are endowed with an internalization mechanism to rehearse social signals and with a goal source selector to actively query for social guidance. The present manuscript is organized in two parts. In the first part, we focus on the design of teachable autotelic agents and attempt to leverage the most important properties that would later serve the social interaction. Namely, we introduce predicate-based autotelic agents, a novel family of autotelic agents that represent their goals using spatial binary predicates. These insights were based on the Mandlerian view on the prelinguistic concept acquisition suggesting that toddlers are endowed with some innate mechanisms enabling them to translate spatio-temporal information into an iconic static form. We show that the underlying semantic representation plays a pivotal role between raw sensory inputs and language inputs, enabling the decoupling of sensorimotor learning and language grounding. We also investigate the design of such agents' policies and state-action value functions, and argue that combining Graph Neural Networks (GNNs) with relational predicates provides a light computational scheme to transfer efficiently between skills. In the second part, we formalize social interactions as a goal exploration process. We introduce Help Me Explore (HME), a novel social interaction protocol where an expert social partner progressively guides the learning agent beyond its zone of proximal development (ZPD). The agent actively selects to query its social partner whenever it estimates that it is not progressing enough alone. It eventually internalizes the social signals, becomes less dependent on its social partner and maximizes its control over its goal space
Wang, Lianfa. "Improving the confidence of CFD results by deep learning." Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLM008.
Повний текст джерелаComputational Fluid Dynamics (CFD) has become an indispensable tool for studying complex flow phenomena in both research and industry over the years. The accuracy of CFD simulations depends on various parameters – geometry, mesh, schemes, solvers, etc. – as well as phenomenological knowledge that only an expert CFD engineer can configure and optimize. The objective of this thesis is to propose an AI assistant to help users, whether they are experts or not, to better choose simulation options and ensure the reliability of results for a target flow phenomenon. In this context, deep learning algorithms are explored to identify the characteristics of flows computed on structured and unstructured meshes of complex geometries. Initially, convolutional neural networks (CNNs), known for their ability to extract patterns from im-ages, are used to identify flow phenomena such as vortices and thermal stratification on structured 2D meshes. Although the results obtained on structured meshes are satisfactory, CNNs can only be applied to structured meshes. To overcome this limitation, a graph-based neural network (GNN) framework is proposed. This framework uses the U-Net architecture and a hierarchy of successively refined graphs through the implementation of a multigrid method (AMG) inspired by the one used in the Code_Saturne CFD code. Subsequently, an in-depth study of kernel functions was conducted according to identification accuracy and training efficiency criteria to better filter the different phenomena on unstructured meshes. After comparing available kernel functions in the literature, a new kernel function based on the Gaussian mixture model was proposed. This function is better suited to identifying flow phenomena on unstructured meshes. The superiority of the proposed architecture and kernel function is demonstrated by several numerical experiments identifying 2D vortices and its adaptability to identifying the characteristics of a 3D flow
Liu, Wenzhuo. "Deep Graph Neural Networks for Numerical Simulation of PDEs." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG032.
Повний текст джерелаPartial differential equations (PDEs) are an essential modeling tool for the numerical simulation of complex systems. However, their accurate numerical resolution usually requires a high computational cost. In recent years, deep Learning algorithms have demonstrated impressive successes in learning from examples, and their direct application to databases of existing solutions of a PDE could be a way to tackle the excessive computational cost of classical numerical approaches: Once a neural model has been learned, the computational cost of inference of the solution on new example is very low. However, many issues remain that this Ph.D. thesis investigates, focusing on three major hurdles: handling unstructured meshes, which can hardly be done accurately by simply porting the neural successes on image processing tasks; generalization issues, in particular for Out-of-Distribution examples; and the too high computational costs for generating the training data. We propose three contributions, based on Graph Neural Networks, to tackle these problems: A hierarchical model inspired by the multi-grid techniques of Numerical Analysis; The use of Meta-Learning to improve the performance of Out-of-Distribution data; and Transfer Learning between multi-fidelity datasets to reduce the computational cost of data generation. The proposed approaches are experimentally validated on different physical systems
Sourty, Raphael. "Apprentissage de représentation de graphes de connaissances et enrichissement de modèles de langue pré-entraînés par les graphes de connaissances : approches basées sur les modèles de distillation." Electronic Thesis or Diss., Toulouse 3, 2023. http://www.theses.fr/2023TOU30337.
Повний текст джерелаNatural language processing (NLP) is a rapidly growing field focusing on developing algorithms and systems to understand and manipulate natural language data. The ability to effectively process and analyze natural language data has become increasingly important in recent years as the volume of textual data generated by individuals, organizations, and society as a whole continues to grow significantly. One of the main challenges in NLP is the ability to represent and process knowledge about the world. Knowledge graphs are structures that encode information about entities and the relationships between them, they are a powerful tool that allows to represent knowledge in a structured and formalized way, and provide a holistic understanding of the underlying concepts and their relationships. The ability to learn knowledge graph representations has the potential to transform NLP and other domains that rely on large amounts of structured data. The work conducted in this thesis aims to explore the concept of knowledge distillation and, more specifically, mutual learning for learning distinct and complementary space representations. Our first contribution is proposing a new framework for learning entities and relations on multiple knowledge bases called KD-MKB. The key objective of multi-graph representation learning is to empower the entity and relation models with different graph contexts that potentially bridge distinct semantic contexts. Our approach is based on the theoretical framework of knowledge distillation and mutual learning. It allows for efficient knowledge transfer between KBs while preserving the relational structure of each knowledge graph. We formalize entity and relation inference between KBs as a distillation loss over posterior probability distributions on aligned knowledge. Grounded on this finding, we propose and formalize a cooperative distillation framework where a set of KB models are jointly learned by using hard labels from their own context and soft labels provided by peers. Our second contribution is a method for incorporating rich entity information from knowledge bases into pre-trained language models (PLM). We propose an original cooperative knowledge distillation framework to align the masked language modeling pre-training task of language models and the link prediction objective of KB embedding models. By leveraging the information encoded in knowledge bases, our proposed approach provides a new direction to improve the ability of PLM-based slot-filling systems to handle entities
Ferré, Paul. "Adéquation algorithme-architecture de réseaux de neurones à spikes pour les architectures matérielles massivement parallèles." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30318/document.
Повний текст джерелаThe last decade has seen the re-emergence of machine learning methods based on formal neural networks under the name of deep learning. Although these methods have enabled a major breakthrough in machine learning, several obstacles to the possibility of industrializing these methods persist, notably the need to collect and label a very large amount of data as well as the computing power necessary to perform learning and inference with this type of neural network. In this thesis, we propose to study the adequacy between inference and learning algorithms derived from biological neural networks and massively parallel hardware architectures. We show with three contribution that such adequacy drastically accelerates computation times inherent to neural networks. In our first axis, we study the adequacy of the BCVision software engine developed by Brainchip SAS for GPU platforms. We also propose the introduction of a coarse-to-fine architecture based on complex cells. We show that GPU portage accelerates processing by a factor of seven, while the coarse-to-fine architecture reaches a factor of one thousand. The second contribution presents three algorithms for spike propagation adapted to parallel architectures. We study exhaustively the computational models of these algorithms, allowing the selection or design of the hardware system adapted to the parameters of the desired network. In our third axis we present a method to apply the Spike-Timing-Dependent-Plasticity rule to image data in order to learn visual representations in an unsupervised manner. We show that our approach allows the effective learning a hierarchy of representations relevant to image classification issues, while requiring ten times less data than other approaches in the literature
Giraldo, Zuluaga Jhony Heriberto. "Graph-based Algorithms in Computer Vision, Machine Learning, and Signal Processing." Electronic Thesis or Diss., La Rochelle, 2022. http://www.theses.fr/2022LAROS037.
Повний текст джерелаGraph representation learning and its applications have gained significant attention in recent years. Notably, Graph Neural Networks (GNNs) and Graph Signal Processing (GSP) have been extensively studied. GNNs extend the concepts of convolutional neural networks to non-Euclidean data modeled as graphs. Similarly, GSP extends the concepts of classical digital signal processing to signals supported on graphs. GNNs and GSP have numerous applications such as semi-supervised learning, point cloud semantic segmentation, prediction of individual relations in social networks, modeling proteins for drug discovery, image, and video processing. In this thesis, we propose novel approaches in video and image processing, GNNs, and recovery of time-varying graph signals. Our main motivation is to use the geometrical information that we can capture from the data to avoid data hungry methods, i.e., learning with minimal supervision. All our contributions rely heavily on the developments of GSP and spectral graph theory. In particular, the sampling and reconstruction theory of graph signals play a central role in this thesis. The main contributions of this thesis are summarized as follows: 1) we propose new algorithms for moving object segmentation using concepts of GSP and GNNs, 2) we propose a new algorithm for weakly-supervised semantic segmentation using hypergraph neural networks, 3) we propose and analyze GNNs using concepts from GSP and spectral graph theory, and 4) we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples
Tiano, Donato. "Learning models on healthcare data with quality indicators." Electronic Thesis or Diss., Lyon 1, 2022. http://www.theses.fr/2022LYO10182.
Повний текст джерелаTime series are collections of data obtained through measurements over time. The purpose of this data is to provide food for thought for event extraction and to represent them in an understandable pattern for later use. The whole process of discovering and extracting patterns from the dataset is carried out with several extraction techniques, including machine learning, statistics, and clustering. This domain is then divided by the number of sources adopted to monitor a phenomenon. Univariate time series when the data source is single and multivariate time series when the data source is multiple. The time series is not a simple structure. Each observation in the series has a strong relationship with the other observations. This interrelationship is the main characteristic of time series, and any time series extraction operation has to deal with it. The solution adopted to manage the interrelationship is related to the extraction operations. The main problem with these techniques is that they do not adopt any pre-processing operation on the time series. Raw time series have many undesirable effects, such as noisy points or the huge memory space required for long series. We propose new data mining techniques based on the adoption of the most representative features of time series to obtain new models from the data. The adoption of features has a profound impact on the scalability of systems. Indeed, the extraction of a feature from the time series allows for the reduction of an entire series to a single value. Therefore, it allows for improving the management of time series, reducing the complexity of solutions in terms of time and space. FeatTS proposes a clustering method for univariate time series that extracts the most representative features of the series. FeatTS aims to adopt the features by converting them into graph networks to extract interrelationships between signals. A co-occurrence matrix merges all detected communities. The intuition is that if two time series are similar, they often belong to the same community, and the co-occurrence matrix reveals this. In Time2Feat, we create a new multivariate time series clustering. Time2Feat offers two different extractions to improve the quality of the features. The first type of extraction is called Intra-Signal Features Extraction and allows to obtain of features from each signal of the multivariate time series. Inter-Signal Features Extraction is used to obtain features by considering pairs of signals belonging to the same multivariate time series. Both methods provide interpretable features, which makes further analysis possible. The whole time series clustering process is lighter, which reduces the time needed to obtain the final cluster. Both solutions represent the state of the art in their field. In AnomalyFeat, we propose an algorithm to reveal anomalies from univariate time series. The characteristic of this algorithm is the ability to work among online time series, i.e. each value of the series is obtained in streaming. In the continuity of previous solutions, we adopt the functionality of revealing anomalies in the series. With AnomalyFeat, we unify the two most popular algorithms for anomaly detection: clustering and recurrent neural network. We seek to discover the density area of the new point obtained with clustering