Literatura científica selecionada sobre o tema "Optimisation des hyperparamètres"
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Artigos de revistas sobre o assunto "Optimisation des hyperparamètres"
Rajaoui, Nordine. "BAYÉSIEN VERSUS CMA-ES : OPTIMISATION DES HYPERPARAMÈTRES ML". Management & Data Science, 2023. http://dx.doi.org/10.36863/mds.a.24309.
Texto completo da fonteRAJAOUI, Nordine. "BAYÉSIEN VERSUS CMA-ES : OPTIMISATION DES HYPERPARAMÈTRES ML [PARTIE 2]". Management & Data Science, 2023. http://dx.doi.org/10.36863/mds.a.25154.
Texto completo da fonteTeses / dissertações sobre o assunto "Optimisation des hyperparamètres"
Khessiba, Souhir. "Stratégies d’optimisation des hyper-paramètres de réseaux de neurones appliqués aux signaux temporels biomédicaux". Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAE003.
Texto completo da fonteThis thesis focuses on optimizing the hyperparameters of convolutional neural networks (CNNs) in the medical domain, proposing an innovative approach to improve the performance of decision-making models in the biomedical field. Through the use of a hybrid approach, GS-TPE, to effectively adjust the hyperparameters of complex neural network models, this research has demonstrated significant improvements in the classification of temporal biomedical signals, such as vigilance states, from physiological signals such as electroencephalogram (EEG). Furthermore, by introducing a new DNN architecture, STGCN, for the classification of gestures associated with pathologies such as knee osteoarthritis and Parkinson's disease from video gait analysis, these works offer new perspectives for enhancing medical diagnosis and management through advancements in artificial intelligence
Merasli, Alexandre. "Reconstruction d’images TEP par des méthodes d’optimisation hybrides utilisant un réseau de neurones non supervisé et de l'information anatomique". Electronic Thesis or Diss., Nantes Université, 2024. http://www.theses.fr/2024NANU1003.
Texto completo da fontePET is a functional imaging modality used in oncology to obtain a quantitative image of the distribution of a radiotracer injected into a patient. The raw PET data are characterized by a high level of noise and modest spatial resolution, compared to anatomical imaging modalities such as MRI or CT. In addition, standard methods for image reconstruction from the PET raw data introduce a positive bias in low activity regions, especially when dealing with low statistics acquisitions (highly noisy data). In this work, a new reconstruction algorithm, called DNA, has been developed. Using the ADMM algorithm, DNA combines the recently proposed Deep Image Prior (DIP) method to limit noise propagation and improve spatial resolution by using anatomical information, and a bias reduction method developed for low statistics PET imaging. However, the use of DIP and ADMM algorithms requires the tuning of many hyperparameters, which are often selected manually. A study has been carried out to tune some of them automatically, using methods that could benefit other algorithms. Finally, the use of anatomical information, especially with DIP, allows an improvement of the PET image quality, but can generate artifacts when information from one modality does not spatially match with the other. This is particularly the case when tumors have different anatomical and functional contours. Two methods have been developed to remove these artifacts while trying to preserve the useful information provided by the anatomical modality
Bardenet, Rémi. "Contributions à l'apprentissage et l'inférence adaptatifs : Applications à l'ajustement d'hyperparamètres et à la physique des astroparticules". Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00766107.
Texto completo da fonteBardenet, Rémi. "Towards adaptive learning and inference : applications to hyperparameter tuning and astroparticle physics". Thesis, Paris 11, 2012. http://www.theses.fr/2012PA112307.
Texto completo da fonteInference and optimization algorithms usually have hyperparameters that require to be tuned in order to achieve efficiency. We consider here different approaches to efficiently automatize the hyperparameter tuning step by learning online the structure of the addressed problem. The first half of this thesis is devoted to hyperparameter tuning in machine learning. After presenting and improving the generic sequential model-based optimization (SMBO) framework, we show that SMBO successfully applies to the task of tuning the numerous hyperparameters of deep belief networks. We then propose an algorithm that performs tuning across datasets, mimicking the memory that humans have of past experiments with the same algorithm on different datasets. The second half of this thesis deals with adaptive Markov chain Monte Carlo (MCMC) algorithms, sampling-based algorithms that explore complex probability distributions while self-tuning their internal parameters on the fly. We start by describing the Pierre Auger observatory, a large-scale particle physics experiment dedicated to the observation of atmospheric showers triggered by cosmic rays. The models involved in the analysis of Auger data motivated our study of adaptive MCMC. We derive the first part of the Auger generative model and introduce a procedure to perform inference on shower parameters that requires only this bottom part. Our model inherently suffers from label switching, a common difficulty in MCMC inference, which makes marginal inference useless because of redundant modes of the target distribution. After reviewing existing solutions to label switching, we propose AMOR, the first adaptive MCMC algorithm with online relabeling. We give both an empirical and theoretical study of AMOR, unveiling interesting links between relabeling algorithms and vector quantization
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.
Texto completo da fonteIn 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