We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| remigenet/tkan | canonical | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| run_test | Not yet run | remigenet/tkan/run_tests.py pointer only (licence: NOASSERTION) · get_code("1d4060eeb28f365a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recurrent Neural Networks (RNNs) have revolutionized many areas of machine learning, particularly in natural language and data sequence processing. Long Short-Term Memory (LSTM) has demonstrated its ability to capture long-term dependencies in sequential data. Inspired by the Kolmogorov-Arnold Networks (KANs) a promising alternatives to Multi-Layer Perceptrons (MLPs), we proposed a new neural networks architecture inspired by KAN and the LSTM, the Temporal Kolomogorov-Arnold Networks (TKANs). TKANs combined the strenght of both networks, it is composed of Recurring Kolmogorov-Arnold Networks (RKANs) Layers embedding memory management. This innovation enables us to perform multi-step time series forecasting with enhanced accuracy and efficiency. By addressing the limitations of traditional models in handling complex sequential patterns, the TKAN architecture offers significant potential for advancements in fields requiring more than one step ahead forecasting.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2405.07344")
get_code_for_paper("2405.07344")
have("2405.07344")
Connect an agent — have() is free.