Long-Range MIMO Channel Prediction Using Recurrent Neural Networks

2020 ◽  
Author(s):  
Wei Jiang ◽  
Mathias Strufe ◽  
Hans Dieter Schotten
Keyword(s):  
Neural Networks ◽  
Long Range ◽  
Recurrent Neural Networks ◽  
Mimo Channel ◽  
Channel Prediction

Massive MIMO Channel Prediction Using Recurrent Neural Networks

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Joel Poncha Lemayian ◽  
Jehad M. Hamamreh
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Massive Mimo ◽  
Mimo Channel ◽  
Channel Prediction

scholarly journals Generating Metrically Accurate Homeric Poetry with Recurrent Neural Networks

2020 ◽  
Vol 2 (1) ◽  
pp. 1-25
Author(s):  
Annie K Lamar
Keyword(s):  
Neural Networks ◽  
Long Range ◽  
Recurrent Neural Networks ◽  
Future Research ◽  
Expert Evaluation ◽  
Semantic Coherence ◽  
Metrical Analysis

We investigate the generation of metrically accurate Homeric poetry using recurrent neural networks (RNN). We assess two models: a basic encoder-decoder RNN and the hierarchical recurrent encoderdecoder model (HRED). We assess the quality of the generated lines of poetry using quantitative metrical analysis and expert evaluation. This evaluation reveals that while the basic encoder-decoder is able to capture complex poetic meter, it under performs in terms of semantic coherence. The HRED model, however, produces more semantically coherent lines of poetry but is unable to capture the meter. Our research highlights the importance of expert evaluation and suggests that future research should focus on encoder-decoder models that balance various types of input – both immediate and long-range.

Spike timing-dependent plasticity in sparse recurrent neural networks

2014 ◽  
Vol 1 ◽  
pp. 485-488
Author(s):  
Hideyuki Kato ◽  
Tohru Ikeguchi
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity

Direct Adaptive Control of Process Systems Using Recurrent Neural Networks

1992 ◽  
Author(s):  
Sanjay Parthasarathy ◽  
Alexander G. Parlos ◽  
Amir F. Atiya
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Recurrent Neural Networks ◽  
Process Systems ◽  
Direct Adaptive Control

L2 approximation properties of recurrent neural networks

1997 ◽  
Author(s):  
A. Ruiz ◽  
D.H. Owens ◽  
S. Townley
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Approximation Properties

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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