Zobrazeno 1 - 10
of 3 930
pro vyhledávání: '"Genêt A"'
We present the Finite Element Neural Network Interpolation (FENNI) framework, a sparse neural network architecture extending previous work on Embedded Finite Element Neural Networks (EFENN) introduced with the Hierarchical Deep-learning Neural Networ
Externí odkaz:
http://arxiv.org/abs/2412.05719
This work introduces a hybrid approach that combines the Proper Generalised Decomposition (PGD) with deep learning techniques to provide real-time solutions for parametrised mechanics problems. By relying on a tensor decomposition, the proposed metho
Externí odkaz:
http://arxiv.org/abs/2412.05714
Autor:
Genet, Remi, Inzirillo, Hugo
We introduce a new method inspired by Adam that enhances convergence speed and achieves better loss function minima. Traditional optimizers, including Adam, apply uniform or globally adjusted learning rates across neural networks without considering
Externí odkaz:
http://arxiv.org/abs/2410.24216
Autor:
Genet, Remi, Inzirillo, Hugo
Recent research has challenged the necessity of complex deep learning architectures for time series forecasting, demonstrating that simple linear models can often outperform sophisticated approaches. Building upon this insight, we introduce a novel a
Externí odkaz:
http://arxiv.org/abs/2410.21448
Autor:
Inzirillo, Hugo, Genet, Remi
This paper introduces KAMoE, a novel Mixture of Experts (MoE) framework based on Gated Residual Kolmogorov-Arnold Networks (GRKAN). We propose GRKAN as an alternative to the traditional gating function, aiming to enhance efficiency and interpretabili
Externí odkaz:
http://arxiv.org/abs/2409.15161
Knowledge graphs (KGs) have recently been used for many tools and applications, making them rich resources in structured format. However, in the real world, KGs grow due to the additions of new knowledge in the form of entities and relations, making
Externí odkaz:
http://arxiv.org/abs/2409.04572
The NFDI4DataScience (NFDI4DS) project aims to enhance the accessibility and interoperability of research data within Data Science (DS) and Artificial Intelligence (AI) by connecting digital artifacts and ensuring they adhere to FAIR (Findable, Acces
Externí odkaz:
http://arxiv.org/abs/2408.08698
Autor:
Avriller, R., Genet, C.
We unveil the key-role of dimensionality in describing chiroptical properties of molecules embedded inside an optical Fabry-P\'erot cavity. For a 2D-layer configuration, we show that the interplay between molecular chirality and spatial dispersion of
Externí odkaz:
http://arxiv.org/abs/2408.01275
Autor:
Inzirillo, Hugo, Genet, Remi
We propose a novel approach that enhances multivariate function approximation using learnable path signatures and Kolmogorov-Arnold networks (KANs). We enhance the learning capabilities of these networks by weighting the values obtained by KANs using
Externí odkaz:
http://arxiv.org/abs/2406.17890
Autor:
Genet, Remi, Inzirillo, Hugo
Capturing complex temporal patterns and relationships within multivariate data streams is a difficult task. We propose the Temporal Kolmogorov-Arnold Transformer (TKAT), a novel attention-based architecture designed to address this task using Tempora
Externí odkaz:
http://arxiv.org/abs/2406.02486