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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Semiotic Aggregation In Deep Learning, Bogdan Muşat, Răzvan Andonie
Semiotic Aggregation In Deep Learning, Bogdan Muşat, Răzvan Andonie
All Faculty Scholarship for the College of the Sciences
Convolutional neural networks utilize a hierarchy of neural network layers. The statistical aspects of information concentration in successive layers can bring an insight into the feature abstraction process. We analyze the saliency maps of these layers from the perspective of semiotics, also known as the study of signs and sign-using behavior. In computational semiotics, this aggregation operation (known as superization) is accompanied by a decrease of spatial entropy: signs are aggregated into supersign. Using spatial entropy, we compute the information content of the saliency maps and study the superization processes which take place between successive layers of the network. In …
Weighted Random Search For Cnn Hyperparameter Optimization, Rǎzvan Andonie, Adrian-Cǎtǎlin Florea
Weighted Random Search For Cnn Hyperparameter Optimization, Rǎzvan Andonie, Adrian-Cǎtǎlin Florea
All Faculty Scholarship for the College of the Sciences
Nearly all model algorithms used in machine learning use two different sets of parameters: the training parameters and the meta-parameters (hyperparameters). While the training parameters are learned during the training phase, the values of the hyperparameters have to be specified before learning starts. For a given dataset, we would like to find the optimal combination of hyperparameter values, in a reasonable amount of time. This is a challenging task because of its computational complexity. In previous work, we introduced the Weighted Random Search (WRS) method, a combination of Random Search (RS) and probabilistic greedy heuristic. In the current paper, we …