1 INTRODUCTION  
2 RELATED WORK  
3 BACKGROUND THEORY  
3.1 Artificial Neural Networks  
3.2 Convolutional Neural Nets  
4 METHOD DESCRIPTION  
4.1 Pre-processing  
4.2 First Convolution  
4.3 Second Convolution  
4.4 Flatten Layer  
4.5 Hidden Layer  
4.6 Softmax Layer  
5 DATA  
5.1 OGLE-III  
5.2 The Vista Variable in the Vía Láctea  
5.3 CoRoT  
6 DATA PRE-PROCESSING  
6.1 Time & Magnitude Difference  
6.2 Light Curve Padding  
6.3 A Light Curve Data Augmentation Model  
7 PARAMETER INITIALIZATION  
7.1 Parameters  
7.2 Layers  
7.3 Activation Functions  
7.4 Dropout  
8 RESULTS  
8.1 Computational Run Time  
8.3 Results with Subclasses of Variability  
9 CONCLUSIONS