scholarly journals Neural networks learn to detect and emulate sorting algorithms from images of their execution traces

2020 ◽  
Vol 126 ◽  
pp. 106350
Author(s):  
Cătălin F. Perticas ◽  
Bipin Indurkhya
Keyword(s):  
Neural Networks ◽  
Execution Traces ◽  
Sorting Algorithms

scholarly journals Embedding and classifying test execution traces using neural networks

IET Software ◽  
2021 ◽  
Author(s):  
Foivos Tsimpourlas ◽  
Gwenyth Rooijackers ◽  
Ajitha Rajan ◽  
Miltiadis Allamanis
Keyword(s):  
Neural Networks ◽  
Test Execution ◽  
Execution Traces

scholarly journals Using deep neural networks to detect complex spikes of cerebellar Purkinje cells

2020 ◽  
Vol 123 (6) ◽  
pp. 2217-2234
Author(s):  
Akshay Markanday ◽  
Joachim Bellet ◽  
Marie E. Bellet ◽  
Junya Inoue ◽  
Ziad M. Hafed ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Purkinje Cell ◽  
Purkinje Cells ◽  
Deep Neural Networks ◽  
Learning Approach ◽  
Sorting Algorithms ◽  
Accuracy Level ◽  
Cerebellar Purkinje Cells ◽  
Complex Spikes

Purkinje cell “complex spikes,” fired at perplexingly low rates, play a crucial role in cerebellum-based motor learning. Careful interpretations of these spikes require manually detecting them, since conventional online or offline spike sorting algorithms are optimized for classifying much simpler waveform morphologies. We present a novel deep learning approach for identifying complex spikes, which also measures additional relevant neurophysiological features, with an accuracy level matching that of human experts yet with very little time expenditure.

scholarly journals Verification of RNN-Based Neural Agent-Environment Systems

2019 ◽  
Vol 33 ◽  
pp. 6006-6013 ◽  
Author(s):  
Michael E. Akintunde ◽  
Andreea Kevorchian ◽  
Alessio Lomuscio ◽  
Edoardo Pirovano
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Network ◽  
Experimental Results ◽  
Feed Forward ◽  
Execution Traces ◽  
Verification Problem ◽  
Complete Procedure ◽  
Feed Forward Neural Networks

We introduce agent-environment systems where the agent is stateful and executing a ReLU recurrent neural network. We define and study their verification problem by providing equivalences of recurrent and feed-forward neural networks on bounded execution traces. We give a sound and complete procedure for their verification against properties specified in a simplified version of LTL on bounded executions. We present an implementation and discuss the experimental results obtained.

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