MLP-KAN: Unifying Deep Representation and Function Learning
Authors: name: Yunhong He, name: Yifeng Xie, name: Zhengqing Yuan, name: Lichao Sun

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.