Zobrazeno 1 - 10
of 770
pro vyhledávání: '"Kofinas, P."'
Autor:
Huang, Qi, Mezzi, Emanuele, Mutlu, Osman, Kofinas, Miltiadis, Prasad, Vidya, Khan, Shadnan Azwad, Ranguelova, Elena, van Stein, Niki
We introduce a novel metric for measuring semantic continuity in Explainable AI methods and machine learning models. We posit that for models to be truly interpretable and trustworthy, similar inputs should yield similar explanations, reflecting a co
Externí odkaz:
http://arxiv.org/abs/2407.12950
Hybrid dynamical systems are prevalent in science and engineering to express complex systems with continuous and discrete states. To learn the laws of systems, all previous methods for equation discovery in hybrid systems follow a two-stage paradigm,
Externí odkaz:
http://arxiv.org/abs/2406.03818
Autor:
Kofinas, Miltiadis, Knyazev, Boris, Zhang, Yan, Chen, Yunlu, Burghouts, Gertjan J., Gavves, Efstratios, Snoek, Cees G. M., Zhang, David W.
Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural network weights, and predicting generalization errors. However, existing ap
Externí odkaz:
http://arxiv.org/abs/2403.12143
Autor:
Papa, Samuele, Valperga, Riccardo, Knigge, David, Kofinas, Miltiadis, Lippe, Phillip, Sonke, Jan-Jakob, Gavves, Efstratios
Neural fields (NeFs) have recently emerged as a versatile method for modeling signals of various modalities, including images, shapes, and scenes. Subsequently, a number of works have explored the use of NeFs as representations for downstream tasks,
Externí odkaz:
http://arxiv.org/abs/2312.10531
Autor:
Shamsian, Aviv, Zhang, David W., Navon, Aviv, Zhang, Yan, Kofinas, Miltiadis, Achituve, Idan, Valperga, Riccardo, Burghouts, Gertjan J., Gavves, Efstratios, Snoek, Cees G. M., Fetaya, Ethan, Chechik, Gal, Maron, Haggai
Learning in weight spaces, where neural networks process the weights of other deep neural networks, has emerged as a promising research direction with applications in various fields, from analyzing and editing neural fields and implicit neural repres
Externí odkaz:
http://arxiv.org/abs/2311.08851
Systems of interacting objects often evolve under the influence of field effects that govern their dynamics, yet previous works have abstracted away from such effects, and assume that systems evolve in a vacuum. In this work, we focus on discovering
Externí odkaz:
http://arxiv.org/abs/2310.20679
Autor:
Papa, Samuele, Knigge, David M., Valperga, Riccardo, Moriakov, Nikita, Kofinas, Miltos, Sonke, Jan-Jakob, Gavves, Efstratios
Conventional Computed Tomography (CT) methods require large numbers of noise-free projections for accurate density reconstructions, limiting their applicability to the more complex class of Cone Beam Geometry CT (CBCT) reconstruction. Recently, deep
Externí odkaz:
http://arxiv.org/abs/2307.08351
Dynamical systems with complex behaviours, e.g. immune system cells interacting with a pathogen, are commonly modelled by splitting the behaviour into different regimes, or modes, each with simpler dynamics, and then learning the switching behaviour
Externí odkaz:
http://arxiv.org/abs/2306.00370
Autor:
Kofinas, C. E., Papistas, A. I.
For a positive integer $n$, with $n \geq 4$, let $R_{n}$ be a free (nilpotent of class 2)-by-abelian and abelian-by-(nilpotent of class 2) Lie algebra of rank $n$. We show that the subgroup of Aut$(R_{n})$ generated by the tame automorphisms and a co
Externí odkaz:
http://arxiv.org/abs/2212.05957
Autor:
Eleni Avramidou, Konstantinos Terlemes, Afroditi Lymperopoulou, Georgios Katsanos, Nikolaos Antoniadis, Athanasios Kofinas, Stella Vasileiadou, Konstantina-Eleni Karakasi, Georgios Tsoulfas
Publikováno v:
Livers, Vol 4, Iss 1, Pp 119-137 (2024)
Since the end of the 20th century and the establishment of minimally invasive techniques, they have become the preferred operative method by many surgeons. These techniques were applied to liver surgery for the first time in 1991, while as far as tra
Externí odkaz:
https://doaj.org/article/dea782ccf6034c18be682376b242b191
