Summary of A Gated Residual Kolmogorov-arnold Networks For Mixtures Of Experts, by Hugo Inzirillo and Remi Genet
A Gated Residual Kolmogorov-Arnold Networks for Mixtures of Experts
by Hugo Inzirillo, Remi Genet
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Mixture of Experts (MoE) framework called KAMoE is introduced, built on top of Gated Residual Kolmogorov-Arnold Networks (GRKAN). The traditional gating function is replaced with GRKAN to enhance efficiency and interpretability in MoE modeling. Experiments on digital asset markets and real estate valuation show that KAMoE outperforms traditional MoE architectures across various tasks and model types, with GRKAN performing better than standard Gating Residual Networks, particularly in LSTM-based models for sequential tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoE is a machine learning technique that combines multiple models to make predictions. This paper introduces a new way of doing MoE called KAMoE, which uses something called GRKAN instead of the usual gating function. The authors tested KAMoE on two different types of data and showed that it works better than the old way of doing MoE. |
Keywords
» Artificial intelligence » Lstm » Machine learning » Mixture of experts
