Comparison of hybrid recurrent neural networks and dual‐polarization models of valve regulated lead acid battery

Author(s):  
Adrian Chmielewski ◽  
Jakub Możaryn ◽  
Piotr Piórkowski ◽  
Jacek Dybała
2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>


2013 ◽  
Vol 12 (11) ◽  
pp. 2175-2182 ◽  
Author(s):  
Jiakuan Yang ◽  
Xinfeng Zhu ◽  
Lei Li ◽  
Jianwen Liu ◽  
Ramachandran Vasant Kumar

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