title: MLP-KAN: Unifying Deep Representation and Function Learning
summary: Recent advancements in both representation learning and function learning
  have demonstrated substantial promise across diverse domains of artificial
  intelligence. However, the effective integration of these paradigms poses a
  significant challenge, particularly in cases where users must manually decide
  whether to apply a representation learning or function learning model based on
  dataset characteristics. To address this issue, we introduce MLP-KAN, 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 (KANs) for function learning within a
  Mixture-of-Experts (MoE) architecture, MLP-KAN dynamically adapts to the
  specific characteristics of the task at hand, ensuring optimal performance.
  Embedded within a transformer-based framework, our work achieves remarkable
  results on four widely-used datasets across diverse domains. Extensive
  experimental evaluation demonstrates its superior versatility, delivering
  competitive performance across both deep representation and function learning
  tasks. These findings highlight the potential of MLP-KAN to simplify the model
  selection process, offering a comprehensive, adaptable solution across various
  domains. Our code and weights are available at
  https://github.com/DLYuanGod/MLP-KAN.
paper_authors._id: 6704617783f1ca2d2dccb67e
paper_authors.name: Yunhong He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-08T07:25:10.837Z
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paper_authors.avatarUrl: /avatars/b2f2fe6cd185a12483217ff099daca06.svg
paper_authors.fullname: Yunhong He
paper_authors.type: user
paper_authors.user: yunhong1
paper_authors._id: 6704617783f1ca2d2dccb67f
paper_authors.name: Yifeng Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-08T07:25:16.291Z
paper_authors._id: 63ce7b5781065f5e3fff8c30
paper_authors.avatarUrl: /avatars/a394f61554bd21b0545a240821e25347.svg
paper_authors.fullname: Yifeng Xie
paper_authors.type: user
paper_authors.user: Evfidiw
paper_authors._id: 6704617783f1ca2d2dccb680
paper_authors.name: Zhengqing Yuan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-08T07:25:27.350Z
paper_authors._id: 62f3cc40b3e6016d46027e1d
paper_authors.avatarUrl: /avatars/9b26d2fd5e8574f633ffd97af1700b45.svg
paper_authors.fullname: YuanZhengqing
paper_authors.type: user
paper_authors.user: YuanZhengqing
paper_authors._id: 6704617783f1ca2d2dccb681
paper_authors.name: Lichao Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-08T07:25:33.102Z
paper_authors._id: 65a52766215aabac489e3468
paper_authors.avatarUrl: /avatars/fe05e22cd7e12e961296426434e17c76.svg
paper_authors.fullname: Lichao Sun
paper_authors.type: user
paper_authors.user: sunlichao137
paper_title: MLP-KAN: Unifying Deep Representation and Function Learning
paper_summary: Recent advancements in both representation learning and function learning
  have demonstrated substantial promise across diverse domains of artificial
  intelligence. However, the effective integration of these paradigms poses a
  significant challenge, particularly in cases where users must manually decide
  whether to apply a representation learning or function learning model based on
  dataset characteristics. To address this issue, we introduce MLP-KAN, 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 (KANs) for function learning within a
  Mixture-of-Experts (MoE) architecture, MLP-KAN dynamically adapts to the
  specific characteristics of the task at hand, ensuring optimal performance.
  Embedded within a transformer-based framework, our work achieves remarkable
  results on four widely-used datasets across diverse domains. Extensive
  experimental evaluation demonstrates its superior versatility, delivering
  competitive performance across both deep representation and function learning
  tasks. These findings highlight the potential of MLP-KAN to simplify the model
  selection process, offering a comprehensive, adaptable solution across various
  domains. Our code and weights are available at
  https://github.com/DLYuanGod/MLP-KAN.
paper_ai_summary: MLP-KAN integrates MLPs and KANs in a MoE architecture within a transformer framework to adaptively handle both representation and function learning tasks, achieving competitive results across diverse datasets.
paper_ai_keywords: Multi-Layer Perceptrons MLPs Kolmogorov-Arnold Networks KANs Mixture-of-Experts MoE transformer-based framework