Experimental and Theoretical Study on the Robustification of Acoustic Emission Inspection with Recurrent Neural Networks

2018 ◽  
pp. 39-44
Author(s):  
Christopher Hsu ◽  
Mulugeta Haile ◽  
Natasha Bradley ◽  
John Chen
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Recurrent Neural Networks ◽  
Theoretical Study

scholarly journals An automatic microseismic or acoustic emission arrival identification scheme with deep recurrent neural networks

2017 ◽  
Vol 212 (2) ◽  
pp. 1389-1397 ◽  
Author(s):  
Jing Zheng ◽  
Jiren Lu ◽  
Suping Peng ◽  
Tianqi Jiang
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Recurrent Neural Networks ◽  
Identification Scheme

Theoretical Study of Oscillator Neurons in Recurrent Neural Networks

2018 ◽  
Vol 29 (11) ◽  
pp. 5242-5248 ◽  
Author(s):  
Lei Zhang ◽  
Zhang Yi ◽  
Shun-ichi Amari
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Theoretical Study

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<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>

LatticeRnn: Recurrent Neural Networks Over Lattices

2016 ◽  
Author(s):  
Faisal Ladhak ◽  
Ankur Gandhe ◽  
Markus Dreyer ◽  
Lambert Mathias ◽  
Ariya Rastrow ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks
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