Liquid Neural Nets (LNNs)
<p>Liquid neural nets (LNNs) are an exciting, relatively new direction in AI/ML research that promises more compact and dynamic neural nets for time series prediction. LNNs offer a new approach to tasks like weather prediction, speech recognition, and autonomous driving. The primary benefit LNNs offer is that they <a href="https://techcrunch.com/2023/08/17/what-is-a-liquid-neural-network-really/" rel="noopener ugc nofollow" target="_blank">continue adapting to new stimuli after training</a>. Additionally, LNNs are robust in noisy conditions and are smaller and more interpretable than their conventional counterparts.</p>
<p>LNNs and similar concepts have been around for a while, but the 2020 paper,<a href="https://arxiv.org/abs/2006.04439" rel="noopener ugc nofollow" target="_blank"> <em>Liquid Time Constant Networks</em></a> catapulted them to the forefront to the AI/ML space. Since then, they’ve cemented themselves as a fascinating direction for time series predictors aimed at increasing individual neurons’ representational capability instead of deriving capability through scale.</p>
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