MLP-KAN: Unifying Deep Representation and Function Learning

Yunhong He, Zhengqing Yuan, Yifeng Xie, Lichao Sun

Keywords: representational learning, functional learning, unified model

TL;DR: MLP-KAN unifies representation and function learning in a Mixture-of-Experts framework, dynamically adapting to tasks without manual model selection, achieving competitive performance across diverse domains.

Abstract: 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.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 4126