The goal of the study is to map the six-dimensional performance metric landscape (Binary Cross-Entropy, Accuracy, Specificity, Precision, Recall, F1)
over the thirteen-dimensional training parameter input space.
There are 393,216,000,000,000 experiments that can be run at the moment, and each one helps map out a point on this surface.
In many situations the nets don't perform well, while in other cases they become highly accurate at predicting the cellular automata.
Test metric results from completed experiments are aggregated and shown on the Analysis page.
Not all information from the training & testing process is saved, so internalize the training loss curves and test set images while they exist in memory.
The Analysis page will eventually include citation capabilities for highlighting, comparing, and referencing performance results.
At that time, there will be an option on the Profile page to be listed as a co-author on this study.
A meta analysis will take place once we hit our goal of 30,000 points! So get over to Training and click that button!
Those results will be used in the next-level, supervised learning study where we explore predicting the test metrics.
The inputs for the study will be the training parameters (instead of the cellular automata start state @ t=0) and the outputs will be the test metrics (instead of the cellular automata end state @ t > 0).