Summary of Tkan: Temporal Kolmogorov-arnold Networks, by Remi Genet and Hugo Inzirillo
TKAN: Temporal Kolmogorov-Arnold Networks
by Remi Genet, Hugo Inzirillo
First submitted to arxiv on: 12 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a new neural networks architecture called Temporal Kolomogorov-Arnold Networks (TKANs), which combines the strengths of Long Short-Term Memory (LSTM) and Kolmogorov-Arnold Networks (KAN). TKANs are composed of Recurring Kolmogorov-Arnold Networks (RKAN) layers, enabling memory management. This innovation allows for multi-step time series forecasting with enhanced accuracy and efficiency. By addressing the limitations of traditional models in handling complex sequential patterns, TKAN architecture offers significant potential for advancements in fields requiring more than one step ahead forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new kind of computer model called Temporal Kolomogorov-Arnold Networks (TKANs). It’s like a combination of two other models that work well together. This new model is good at predicting things that happen multiple steps in the future, which is important for many fields like weather forecasting or stock market predictions. |
Keywords
» Artificial intelligence » Lstm » Time series
