scholarly journals Embedding Multiple Trajectories in Simulated Recurrent Neural Networks in a Self-Organizing Manner

2009 ◽  
Vol 29 (42) ◽  
pp. 13172-13181 ◽  
Author(s):  
J. K. Liu ◽  
D. V. Buonomano
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Self Organizing

scholarly journals The DIAMOND Model: Deep Recurrent Neural Networks for Self-Organizing Robot Control

2020 ◽  
Vol 14 ◽  
Author(s):  
Simón C. Smith ◽  
Richard Dharmadi ◽  
Calum Imrie ◽  
Bailu Si ◽  
J. Michael Herrmann
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Robot Control ◽  
Diamond Model ◽  
Self Organizing

scholarly journals Modeling Word Learning and Processing with Recurrent Neural Networks

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 320
Author(s):  
Claudia Marzi
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Morphological Structure ◽  
Training Data ◽  
Self Organizing Map ◽  
Verb Forms ◽  
Long Short Term Memory ◽  
The One ◽  
Self Organizing

The paper focuses on what two different types of Recurrent Neural Networks, namely a recurrent Long Short-Term Memory and a recurrent variant of self-organizing memories, a Temporal Self-Organizing Map, can tell us about speakers’ learning and processing a set of fully inflected verb forms selected from the top-frequency paradigms of Italian and German. Both architectures, due to the re-entrant layer of temporal connectivity, can develop a strong sensitivity to sequential patterns that are highly attested in the training data. The main goal is to evaluate learning and processing dynamics of verb inflection data in the two neural networks by focusing on the effects of morphological structure on word production and word recognition, as well as on word generalization for untrained verb forms. For both models, results show that production and recognition, as well as generalization, are facilitated for verb forms in regular paradigms. However, the two models are differently influenced by structural effects, with the Temporal Self-Organizing Map more prone to adaptively find a balance between processing issues of learnability and generalization, on the one side, and discriminability on the other side.

Review of Pattern Recognition by Self-Organizing Neural Networks.

1995 ◽  
Vol 40 (11) ◽  
pp. 1110-1110
Author(s):  
Stephen James Thomas
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Self Organizing

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>

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