May 14, 2024, 4:42 a.m. | Remi Genet, Hugo Inzirillo

cs.LG updates on

arXiv:2405.07344v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.lg data dependencies language layer long short-term memory long-term lstm machine machine learning memory natural natural language networks neural networks processing recurrent neural networks temporal type

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