Zobrazeno 1 - 10
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pro vyhledávání: '"P, Arnold"'
Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs) in scientific computing. While PINNs typically use multilayer perceptrons (MLPs) as their underlying architecture, recent ad
Externí odkaz:
http://arxiv.org/abs/2411.06286
This systematic review explores the theoretical foundations, evolution, applications, and future potential of Kolmogorov-Arnold Networks (KAN), a neural network model inspired by the Kolmogorov-Arnold representation theorem. KANs distinguish themselv
Externí odkaz:
http://arxiv.org/abs/2411.06078
Solving partial differential equations (PDEs) is essential in scientific forecasting and fluid dynamics. Traditional approaches often incur expensive computational costs and trade-offs in efficiency and accuracy. Recent deep neural networks improve a
Externí odkaz:
http://arxiv.org/abs/2411.04516
Autor:
Ferdaus, Md Meftahul, Abdelguerfi, Mahdi, Ioup, Elias, Dobson, David, Niles, Kendall N., Pathak, Ken, Sloan, Steven
We introduce KANICE (Kolmogorov-Arnold Networks with Interactive Convolutional Elements), a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Co
Externí odkaz:
http://arxiv.org/abs/2410.17172
The potential of learning models in quantum hardware remains an open question. Yet, the field of quantum machine learning persistently explores how these models can take advantage of quantum implementations. Recently, a new neural network architectur
Externí odkaz:
http://arxiv.org/abs/2410.04435
Deep learning models have revolutionized various domains, with Multi-Layer Perceptrons (MLPs) being a cornerstone for tasks like data regression and image classification. However, a recent study has introduced Kolmogorov-Arnold Networks (KANs) as pro
Externí odkaz:
http://arxiv.org/abs/2410.02077
Kolmogorov-Arnold Networks (KANs) have seen great success in scientific domains thanks to spline activation functions, becoming an alternative to Multi-Layer Perceptrons (MLPs). However, spline functions may not respect symmetry in tasks, which is cr
Externí odkaz:
http://arxiv.org/abs/2410.00435
Inspired by the recently proposed Kolmogorov-Arnold Networks (KANs), we introduce the KAN-based Option Pricing (KANOP) model to value American-style options, building on the conventional Least Square Monte Carlo (LSMC) algorithm. KANs, which are base
Externí odkaz:
http://arxiv.org/abs/2410.00419
Autor:
Yang, Xingyi, Wang, Xinchao
Transformers stand as the cornerstone of mordern deep learning. Traditionally, these models rely on multi-layer perceptron (MLP) layers to mix the information between channels. In this paper, we introduce the Kolmogorov-Arnold Transformer (KAT), a no
Externí odkaz:
http://arxiv.org/abs/2409.10594
Autor:
Knottenbelt, William, Gao, Zeyu, Wray, Rebecca, Zhang, Woody Zhidong, Liu, Jiashuai, Crispin-Ortuzar, Mireia
Survival analysis is a branch of statistics used for modeling the time until a specific event occurs and is widely used in medicine, engineering, finance, and many other fields. When choosing survival models, there is typically a trade-off between pe
Externí odkaz:
http://arxiv.org/abs/2409.04290