Improved recurrent neural networks for online solution of Moore‐Penrose inverse applied to redundant manipulator kinematic control

2018 ◽  
Vol 22 (3) ◽  
pp. 1188-1196
Author(s):  
Xuanjiao Lv ◽  
Zhiguo Tan ◽  
Ke Chen ◽  
Zhi Yang
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Redundant Manipulator ◽  
Kinematic Control

Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks

2012 ◽  
Vol 91 ◽  
pp. 1-10 ◽  
Author(s):  
Shuai Li ◽  
Sanfeng Chen ◽  
Bo Liu ◽  
Yangming Li ◽  
Yongsheng Liang
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Redundant Manipulators ◽  
Kinematic Control

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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