Summary of Kernel Corrector Lstm, by Rodrigo Tuna et al.
Kernel Corrector LSTM
by Rodrigo Tuna, Yassine Baghoussi, Carlos Soares, João Mendes-Moreira
First submitted to arxiv on: 28 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed Corrector LSTM (cLSTM) method has shown promising results in addressing data quality issues in forecasting models, but its computational expense limits its practical application. To address this issue, the authors introduce Kernel Corrector LSTM (KcLSTM), a new Read & Write Machine Learning algorithm that replaces the meta-learner used in cLSTM with a simpler kernel smoothing approach. The KcLSTM method aims to balance forecasting accuracy and training time. Empirical evaluations demonstrate that KcLSTM achieves competitive forecasting accuracy while significantly reducing training time compared to cLSTM and LSTM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make predictions more accurate by fixing problems in the data. This is done using an algorithm called Kernel Corrector LSTM, which is faster than a previous method called Corrector LSTM. The authors tested their algorithm and showed that it works well while being less computationally expensive. This means that people can use it without wasting too much time or computing power. |
Keywords
» Artificial intelligence » Lstm » Machine learning
