Zobrazeno 1 - 10
of 119 526
pro vyhledávání: '"P Kan"'
Time series forecasting is a crucial task that predicts the future values of variables based on historical data. Time series forecasting techniques have been developing in parallel with the machine learning community, from early statistical learning
Externí odkaz:
http://arxiv.org/abs/2408.11306
Autor:
Patra, Subhajit, Panda, Sonali, Parida, Bikram Keshari, Arya, Mahima, Jacobs, Kurt, Bondar, Denys I., Sen, Abhijit
Physics-informed neural networks have proven to be a powerful tool for solving differential equations, leveraging the principles of physics to inform the learning process. However, traditional deep neural networks often face challenges in achieving h
Externí odkaz:
http://arxiv.org/abs/2407.18373
Autor:
Ta, Hoang-Thang, Thai, Duy-Quy, Rahman, Abu Bakar Siddiqur, Sidorov, Grigori, Gelbukh, Alexander
In this paper, we introduce FC-KAN, a Kolmogorov-Arnold Network (KAN) that leverages combinations of popular mathematical functions such as B-splines, wavelets, and radial basis functions on low-dimensional data through element-wise operations. We ex
Externí odkaz:
http://arxiv.org/abs/2409.01763
Neural networks are powerful function approximators, yet their ``black-box" nature often renders them opaque and difficult to interpret. While many post-hoc explanation methods exist, they typically fail to capture the underlying reasoning processes
Externí odkaz:
http://arxiv.org/abs/2408.14780
A major challenge of AI + Science lies in their inherent incompatibility: today's AI is primarily based on connectionism, while science depends on symbolism. To bridge the two worlds, we propose a framework to seamlessly synergize Kolmogorov-Arnold N
Externí odkaz:
http://arxiv.org/abs/2408.10205
Modeling feature interactions is crucial for click-through rate (CTR) prediction, particularly when it comes to high-order explicit interactions. Traditional methods struggle with this task because they often predefine a maximum interaction order, wh
Externí odkaz:
http://arxiv.org/abs/2408.08713
Autor:
Peng, Yiwei, Hooten, Sean, Yu, Xinling, Van Vaerenbergh, Thomas, Yuan, Yuan, Xiao, Xian, Tossoun, Bassem, Cheung, Stanley, Fiorentino, Marco, Beausoleil, Raymond
Kolmogorov-Arnold Networks (KAN) models were recently proposed and claimed to provide improved parameter scaling and interpretability compared to conventional multilayer perceptron (MLP) models. Inspired by the KAN architecture, we propose the Photon
Externí odkaz:
http://arxiv.org/abs/2408.08407
Image-to-Image translation in Generative Artificial Intelligence (Generative AI) has been a central focus of research, with applications spanning healthcare, remote sensing, physics, chemistry, photography, and more. Among the numerous methodologies,
Externí odkaz:
http://arxiv.org/abs/2408.08216
In this paper, we compare the performance of Kolmogorov-Arnold Networks (KAN) and Multi-Layer Perceptron (MLP) networks on irregular or noisy functions. We control the number of parameters and the size of the training samples to ensure a fair compari
Externí odkaz:
http://arxiv.org/abs/2408.07906
Kolmogorov-Arnold Networks (KAN) has recently attracted significant attention as a promising alternative to traditional Multi-Layer Perceptrons (MLP). Despite their theoretical appeal, KAN require validation on large-scale benchmark datasets. Time se
Externí odkaz:
http://arxiv.org/abs/2408.07314