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pro vyhledávání: '"Allmendinger P"'
Evolutionary Multi-Objective Optimization Algorithms (EMOAs) are widely employed to tackle problems with multiple conflicting objectives. Recent research indicates that not all objectives are equally important to the decision-maker (DM). In the conte
Externí odkaz:
http://arxiv.org/abs/2411.04547
This paper explores the integration of ring attractors, a mathematical model inspired by neural circuit dynamics, into the reinforcement learning (RL) action selection process. Ring attractors, as specialized brain-inspired structures that encode spa
Externí odkaz:
http://arxiv.org/abs/2410.03119
The application of large deep learning models in weather forecasting has led to significant advancements in the field, including higher-resolution forecasting and extended prediction periods exemplified by models such as Pangu and Fuxi. Despite these
Externí odkaz:
http://arxiv.org/abs/2409.18885
In the landscape of generative artificial intelligence, diffusion-based models have emerged as a promising method for generating synthetic images. However, the application of diffusion models poses numerous challenges, particularly concerning data av
Externí odkaz:
http://arxiv.org/abs/2406.14429
Autor:
Pricopie, Stefan, Allmendinger, Richard, Lopez-Ibanez, Manuel, Fare, Clyde, Benatan, Matt, Knowles, Joshua
We investigate modifications to Bayesian Optimization for a resource-constrained setting of sequential experimental design where changes to certain design variables of the search space incur a switching cost. This models the scenario where there is a
Externí odkaz:
http://arxiv.org/abs/2405.08973
Generative AI, in general, and synthetic visual data generation, in specific, hold much promise for benefiting surgical training by providing photorealism to simulation environments. Current training methods primarily rely on reading materials and ob
Externí odkaz:
http://arxiv.org/abs/2406.06537
Synthetic data has a key role to play in data sharing by statistical agencies and other generators of statistical data products. Generative Adversarial Networks (GANs), typically applied to image synthesis, are also a promising method for tabular dat
Externí odkaz:
http://arxiv.org/abs/2404.10176
Autor:
Alshmrany, Kaled M., Aldughaim, Mohannad, Wei, Chenfeng, Sweet, Tom, Allmendinger, Richard, Cordeiro, Lucas C.
We present FuSeBMC-AI, a test generation tool grounded in machine learning techniques. FuSeBMC-AI extracts various features from the program and employs support vector machine and neural network models to predict a hybrid approach optimal configurati
Externí odkaz:
http://arxiv.org/abs/2404.06031
Autor:
Lepère, Muriel, Browet, Olivier, Clément, Jean, Vispoel, Bastien, Allmendinger, Pitt, Hayden, Jakob, Eigenmann, Florian, Hugi, Andreas, Mangold, Markus
Publikováno v:
Journal of Quantitative Spectroscopy and Radiative Transfer, Volume 287, 108239 (2022)
To meet the challenges of high-resolution molecular spectroscopy, increasingly sophisticated spectroscopic techniques were developed. For a long time FTIR and laser-based spectroscopies were used for these studies. The recent development of dual-comb
Externí odkaz:
http://arxiv.org/abs/2403.02720
In the landscape of generative artificial intelligence, diffusion-based models present challenges for socio-technical systems in data requirements and privacy. Traditional approaches like federated learning distribute the learning process but strain
Externí odkaz:
http://arxiv.org/abs/2402.19105