Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Sherry, Ferdia"'
Autor:
Canizares, Priscilla, Murari, Davide, Schönlieb, Carola-Bibiane, Sherry, Ferdia, Shumaylov, Zakhar
Hamilton's equations of motion form a fundamental framework in various branches of physics, including astronomy, quantum mechanics, particle physics, and climate science. Classical numerical solvers are typically employed to compute the time evolutio
Externí odkaz:
http://arxiv.org/abs/2410.18262
Autor:
Shumaylov, Zakhar, Zaika, Peter, Rowbottom, James, Sherry, Ferdia, Weber, Melanie, Schönlieb, Carola-Bibiane
The quest for robust and generalizable machine learning models has driven recent interest in exploiting symmetries through equivariant neural networks. In the context of PDE solvers, recent works have shown that Lie point symmetries can be a useful i
Externí odkaz:
http://arxiv.org/abs/2410.02698
Graph Neural Networks (GNNs) have established themselves as a key component in addressing diverse graph-based tasks. Despite their notable successes, GNNs remain susceptible to input perturbations in the form of adversarial attacks. This paper introd
Externí odkaz:
http://arxiv.org/abs/2311.06942
Plug-and-play (PnP) denoising is a popular iterative framework for solving imaging inverse problems using off-the-shelf image denoisers. Their empirical success has motivated a line of research that seeks to understand the convergence of PnP iterates
Externí odkaz:
http://arxiv.org/abs/2307.09441
Autor:
Sherry, Ferdia, Celledoni, Elena, Ehrhardt, Matthias J., Murari, Davide, Owren, Brynjulf, Schönlieb, Carola-Bibiane
Motivated by classical work on the numerical integration of ordinary differential equations we present a ResNet-styled neural network architecture that encodes non-expansive (1-Lipschitz) operators, as long as the spectral norms of the weights are ap
Externí odkaz:
http://arxiv.org/abs/2306.17332
Neural networks have gained much interest because of their effectiveness in many applications. However, their mathematical properties are generally not well understood. If there is some underlying geometric structure inherent to the data or to the fu
Externí odkaz:
http://arxiv.org/abs/2210.02373
Autor:
Chen, Dongdong, Davies, Mike, Ehrhardt, Matthias J., Schönlieb, Carola-Bibiane, Sherry, Ferdia, Tachella, Julián
From early image processing to modern computational imaging, successful models and algorithms have relied on a fundamental property of natural signals: symmetry. Here symmetry refers to the invariance property of signal sets to transformations such a
Externí odkaz:
http://arxiv.org/abs/2209.01725
Autor:
Sherry, Ferdia, Celledoni, Elena, Ehrhardt, Matthias J., Murari, Davide, Owren, Brynjulf, Schönlieb, Carola-Bibiane
Publikováno v:
In Physica D: Nonlinear Phenomena July 2024 463
Autor:
Celledoni, Elena, Ehrhardt, Matthias J., Etmann, Christian, Owren, Brynjulf, Schönlieb, Carola-Bibiane, Sherry, Ferdia
In recent years the use of convolutional layers to encode an inductive bias (translational equivariance) in neural networks has proven to be a very fruitful idea. The successes of this approach have motivated a line of research into incorporating oth
Externí odkaz:
http://arxiv.org/abs/2102.11504
Autor:
Celledoni, Elena, Ehrhardt, Matthias J., Etmann, Christian, McLachlan, Robert I, Owren, Brynjulf, Schönlieb, Carola-Bibiane, Sherry, Ferdia
Over the past few years, deep learning has risen to the foreground as a topic of massive interest, mainly as a result of successes obtained in solving large-scale image processing tasks. There are multiple challenging mathematical problems involved i
Externí odkaz:
http://arxiv.org/abs/2006.03364