Activation Space Selectable Kolmogorov-Arnold Networks

• URL: http://arxiv.org/abs/2408.08338v1
• Date: Thu, 15 Aug 2024 11:34:05 GMT
• Title: Activation Space Selectable Kolmogorov-Arnold Networks
• Authors: Zhuoqin Yang, Jiansong Zhang, Xiaoling Luo, Zheng Lu, Linlin Shen,
• Abstract summary: Kolmogorov-Arnold Network (KAN), based on nonlinear additive connections, has been proven to achieve performance comparable to Select-based methods. Despite this potential, the use of a single activation function space results in reduced performance of KAN and related works across different tasks. This work contributes to the understanding of the data-centric design of new AI and provides a foundational reference for innovations in KAN-based network architectures.
• Score: 29.450377034478933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The multilayer perceptron (MLP), a fundamental paradigm in current artificial intelligence, is widely applied in fields such as computer vision and natural language processing. However, the recently proposed Kolmogorov-Arnold Network (KAN), based on nonlinear additive connections, has been proven to achieve performance comparable to MLPs with significantly fewer parameters. Despite this potential, the use of a single activation function space results in reduced performance of KAN and related works across different tasks. To address this issue, we propose an activation space Selectable KAN (S-KAN). S-KAN employs an adaptive strategy to choose the possible activation mode for data at each feedforward KAN node. Our approach outperforms baseline methods in seven representative function fitting tasks and significantly surpasses MLP methods with the same level of parameters. Furthermore, we extend the structure of S-KAN and propose an activation space selectable Convolutional KAN (S-ConvKAN), which achieves leading results on four general image classification datasets. Our method mitigates the performance variability of the original KAN across different tasks and demonstrates through extensive experiments that feedforward KANs with selectable activations can achieve or even exceed the performance of MLP-based methods. This work contributes to the understanding of the data-centric design of new AI paradigms and provides a foundational reference for innovations in KAN-based network architectures.

Related papers

• A Survey on Kolmogorov-Arnold Network [0.0]
  Review explores the theoretical foundations, evolution, applications, and future potential of Kolmogorov-Arnold Networks (KAN) KANs distinguish themselves from traditional neural networks by using learnable, spline- parameterized functions instead of fixed activation functions. This paper highlights KAN's role in modern neural architectures and outlines future directions to improve its computational efficiency, interpretability, and scalability in data-intensive applications.
  arXiv  Detail & Related papers  (2024-11-09T05:54:17Z)
• MLP-KAN: Unifying Deep Representation and Function Learning [7.634331640151854]
  We introduce a unified method designed to eliminate the need for manual model selection. By integrating Multi-Layer Perceptrons (MLPs) for representation learning and Kolmogorov-Arnold Networks (KANsogo) for function learning, we achieve remarkable results.
  arXiv  Detail & Related papers  (2024-10-03T22:22:43Z)
• Incorporating Arbitrary Matrix Group Equivariance into KANs [69.30866522377694]
  Kolmogorov-Arnold Networks (KANs) have seen great success in scientific domains. However, spline functions may not respect symmetry in tasks, which is crucial prior knowledge in machine learning. We propose Equivariant Kolmogorov-Arnold Networks (EKAN) to broaden their applicability to more fields.
  arXiv  Detail & Related papers  (2024-10-01T06:34:58Z)
• A preliminary study on continual learning in computer vision using Kolmogorov-Arnold Networks [43.70716358136333]
  Kolmogorov- Networks (KAN) are based on a fundamentally different mathematical framework. KANs address several major issues insio, such as forgetting in continual learning scenarios. We extend the investigation by evaluating the performance of KANs in continual learning tasks within computer vision.
  arXiv  Detail & Related papers  (2024-09-20T14:49:21Z)
• KAN v.s. MLP for Offline Reinforcement Learning [4.3621896506713185]
  Kolmogorov-Arnold Networks (KAN) is an emerging neural network architecture in machine learning. In this paper, we explore the incorporation of KAN into the actor and critic networks for offline reinforcement learning.
  arXiv  Detail & Related papers  (2024-09-15T07:52:44Z)
• Kolmogorov-Arnold Network for Online Reinforcement Learning [0.22615818641180724]
  Kolmogorov-Arnold Networks (KANs) have shown potential as an alternative to Multi-Layer Perceptrons (MLPs) in neural networks. KANs provide universal function approximation with fewer parameters and reduced memory usage.
  arXiv  Detail & Related papers  (2024-08-09T03:32:37Z)
• Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach [51.63921041249406]
  Non-orthogonal multiple access (NOMA) enables multiple users to share the same frequency band, and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) deploying STAR-RIS indoors presents challenges in interference mitigation, power consumption, and real-time configuration. A novel network architecture utilizing multiple access points (APs), STAR-RISs, and NOMA is proposed for indoor communication.
  arXiv  Detail & Related papers  (2024-06-19T07:17:04Z)
• Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures [68.91874045918112]
  adapter-ALBERT is an efficient model optimization for maximal data reuse across different tasks. We demonstrate the advantage of mapping the model to a heterogeneous on-chip memory architecture by performing simulations on a validated NLP edge accelerator.
  arXiv  Detail & Related papers  (2023-03-25T14:40:59Z)
• Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation [98.95383921866096]
  We study the problem of single-channel source separation (SCSS) We focus on cyclostationary signals, which are particularly suitable in a variety of application domains. We propose a deep learning approach using a U-Net architecture, which is competitive with the minimum MSE estimator.
  arXiv  Detail & Related papers  (2022-08-22T14:04:56Z)
• Revisiting GANs by Best-Response Constraint: Perspective, Methodology, and Application [49.66088514485446]
  Best-Response Constraint (BRC) is a general learning framework to explicitly formulate the potential dependency of the generator on the discriminator. We show that even with different motivations and formulations, a variety of existing GANs ALL can be uniformly improved by our flexible BRC methodology.
  arXiv  Detail & Related papers  (2022-05-20T12:42:41Z)
• Consistency and Diversity induced Human Motion Segmentation [231.36289425663702]
  We propose a novel Consistency and Diversity induced human Motion (CDMS) algorithm. Our model factorizes the source and target data into distinct multi-layer feature spaces. A multi-mutual learning strategy is carried out to reduce the domain gap between the source and target data.
  arXiv  Detail & Related papers  (2022-02-10T06:23:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.