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Дисертації з теми "Recherche d'Architectures de Réseaux de Neurones Artificiels":
Egele, Romain. "Optimization of Learning Workflows at Large Scale on High-Performance Computing Systems." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG025.
In the past decade, machine learning has experienced exponential growth, propelled by abundant datasets, algorithmic advancements, and increased computational power. Simultaneously, high-performance computing (HPC) has evolved to meet rising computational demands, offering resources to tackle complex scientific challenges.However, machine learning is often a sequential process, making it difficult to scale on HPC systems. Machine learning workflows are built from modules offering numerous configurable parameters, from data augmentation policies to training procedures and model architectures. This thesis focuses on the hyperparameter optimization of learning workflows on large-scale HPC systems, such as the Polaris at the Argonne Leadership Computing Facility.Key contributions include (1) asynchronous decentralized parallel Bayesian optimization, (2) extension to multi-objective, (3) integration of early discarding, and (4) uncertainty quantification of deep neural networks. Furthermore, an open-source software, DeepHyper, is provided, encapsulating the proposed algorithms to facilitate research and application. The thesis highlights the importance of scalable Bayesian optimization methods for the hyperparameter optimization of learning workflows, which is crucial for effectively harnessing the vast computational resources of modern HPC systems
Clergue, Gérard. "L'avènement de la complexité dans la construction des apprentissages : application à la pédagogie des recherches menées en informatique sur le chaos déterministe et les réseaux de neurones artificiels." Paris 10, 1996. http://www.theses.fr/1996PA100081.
The aim of this thesis is to explore the following areas: - to set out the many new concepts linked to complexity theory, using recent developments in determinist chaos theory. - To evolve means of applying them to theories of human learning and to begin exploring the relevance of these principles and models in the educational field. - The use of connectionist models of artificial neural networks, especially the most dynamic of them, to understand the processes of human cognition. Learning can be seen as a trajectory in the phase’s space of cognitive system of the individual. Into this phase’s space: (a) concepts emerge from the convergence created by existing basins of attraction while the student tries to apply the real to the schemata he has already constructed for himself. (b) At the same time new attractors are formed in the chaotic disorder by adapting to the unpredictable variations of the environment and constantly reshape this landscape. During the learning period the emergence of a new concept appears as a transition of phase from the earlier state of knowledge to the new state. The student has to continuously confront the duality of stability-plasticity which contains as many potential frustrations as successful solutions. This duality cannot be temporarily resolved without taking action, and so we can say with von foerster "if you want knowledge learn to act"
Veniat, Tom. "Neural Architecture Search under Budget Constraints." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS443.
The recent increase in computation power and the ever-growing amount of data available ignited the rise in popularity of deep learning. However, the expertise, the amount of data, and the computing power necessary to build such algorithms as well as the memory footprint and the inference latency of the resulting system are all obstacles preventing the widespread use of these methods. In this thesis, we propose several methods allowing to make a step towards a more efficient and automated procedure to build deep learning models. First, we focus on learning an efficient architecture for image processing problems. We propose a new model in which we can guide the architecture learning procedure by specifying a fixed budget and cost function. Then, we consider the problem of sequence classification, where a model can be even more efficient by dynamically adapting its size to the complexity of the signal to come. We show that both approaches result in significant budget savings. Finally, we tackle the efficiency problem through the lens of transfer learning. Arguing that a learning procedure can be made even more efficient if, instead of starting tabula rasa, it builds on knowledge acquired during previous experiences. We explore modular architectures in the continual learning scenario and present a new benchmark allowing a fine-grained evaluation of different kinds of transfer
Hammadi, Youssef. "Réduction d'un modèle 0D instationnaire et non-linéaire de thermique habitacle pour l’optimisation énergétique des véhicules automobiles." Thesis, Université Paris sciences et lettres, 2020. http://www.theses.fr/2020UPSLM027.
The use of automotive air conditioning leads to a fuel overconsumption. To reduce this overconsumption, we can either work upstream on the technical definitions of the cabin and the HVAC system or optimize control strategies. In both cases, it is essential to build a cabin thermal model that well balances accuracy and complexity. This is the topic of this PhD thesis driven by Renault Group. First, a model reduction methodology is used to build a 0D model starting from a 3D finite element cabin thermal model. This 0D model is based on mass and energy balances on the different cabin walls and air zones. It consists of a nonlinear differential algebraic equations system which can be reinterpreted as a Bond Graph. In addition, the 0D model is based on a weak coupling between the thermal equations and the fluid mechanics ones resulting from CFD calculations (internal airflow and external aerodynamics). Secondly, we apply a machine learning method to the data generated by the 0D model in order to build a reduced 0D model. A design of experiment is considered at this stage. Due to the nonlinearity of the heat exchanges, we have developed an approach which is inspired by the Gappy POD and EIM methods. We use a multiphysics reduced basis that takes several contributions into account (temperatures, enthalpies, heat fluxes and humidities). The resulting reduced model is a hybrid model that couples some of the original physical equations to an artificial neural network. The reduction methodology has been validated on Renault vehicles. The reduced order models have been integrated into a vehicle system-level energetic simulation platform (GREEN) which models different thermics (engine, transmission, cooling system, battery, HVAC, refrigerant circuit, underhood) in order to perform thermal management studies which are of particular importance for electric and hybrid vehicles. The reduced order models have been validated on several scenarios (temperature control for thermal comfort, driving cycles, HVAC coupling) and have achieved CPU gains of up to 99% with average errors of 0.5 °C on temperatures and 0.6% on relative humidities
Fontaine, Nicolas. "Modélisation de système synthétique pour la production de biohydrogène." Thesis, La Réunion, 2015. http://www.theses.fr/2015LARE0016/document.
Hydrogen is a candidate for the next generation fuel with a high energy density and an environment friendly behavior in the energy production phase. Micro-organism based biological production of hydrogen currently suffers low hydrogen production yields because the living cells must sustain different cellular activities other than the hydrogen production to survive. To circumvent this, a team have designed a synthetic cell-free system by combining 13 different enzymes to synthesize hydrogen from cellobiose. This assembly has better yield than microorganism-based systems. We used methods based on differential equations calculations to investigate how the initial conditions and the kinetic parameters of the enzymes influenced the productivity of a such system and, through simulations, to identify those conditions that would optimize hydrogen production starting with cellobiose as substrate. Further, if the kinetic parameters of the component enzymes of such a system are not known, we showed how, using artificial neural network, it is possible to identify alternative models that allow to have an idea of the kinetics of hydrogen production. During our study on the system using cellobiose, other cell-free assemblies were engineered to produce hydrogen from different raw materials. Interested in the reconstruction of synthetic systems, we decided to conceive various tools to help the automation of the assembly and the modelling of these new synthetic networks. This work demonstrates how modeling can help in designing and characterizing cell-free systems in synthetic biology