Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Reu, Teodora"'
Autor:
Kapusniak, Kacper, Potaptchik, Peter, Reu, Teodora, Zhang, Leo, Tong, Alexander, Bronstein, Michael, Bose, Avishek Joey, Di Giovanni, Francesco
Matching objectives underpin the success of modern generative models and rely on constructing conditional paths that transform a source distribution into a target distribution. Despite being a fundamental building block, conditional paths have been d
Externí odkaz:
http://arxiv.org/abs/2405.14780
Graph Neural Networks (GNNs) have demonstrated remarkable success in learning from graph-structured data. However, they face significant limitations in expressive power, struggling with long-range interactions and lacking a principled approach to mod
Externí odkaz:
http://arxiv.org/abs/2306.03561
Denoising diffusion models are a class of generative models which have recently achieved state-of-the-art results across many domains. Gradual noise is added to the data using a diffusion process, which transforms the data distribution into a Gaussia
Externí odkaz:
http://arxiv.org/abs/2305.09605
This paper presents a study of the use of numerical simulation and Bayesian optimisation techniques to investigate the dynamics of celestial systems. Initially, the study focuses on Lagrange points in restricted three-body systems where a 2D three-bo
Externí odkaz:
http://arxiv.org/abs/2303.14835
Autor:
Reu, Teodora
International initiatives such as METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) have collected several multigenomic and clinical data sets to identify the undergoing molecular processes taking place throughout the evolution
Externí odkaz:
http://arxiv.org/abs/2211.15561
Denoising diffusion models are a class of generative models which have recently achieved state-of-the-art results across many domains. Gradual noise is added to the data using a diffusion process, which transforms the data distribution into a Gaussia
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c9955041ccbcd9567d54696b2812da0
http://arxiv.org/abs/2305.09605
http://arxiv.org/abs/2305.09605