Preselection... use cosmic veto? Something different?
* Train on preselected (more challenging) cosmics
* Just try running current model and see which cosmics even pass
** Make pixelmap files for a bunch of cosmics on the grid.
UPS products will soon go live on bluearc
* ANG updates for Caffe model analysis need to be committed
* We can soon deploy on grid
** NB: we'll have to be careful about how we ship model deploy files, they're too big for regular cp
** We want to fix a few things before the next round of training
*** Calibration... PECorr -> GeV...
*** Vertex location/noise scrubbing... turn this off
** Do these things when the new files come out, or before we generate the next pixelmap files
Implement ROOT2LevelDB in novasoft context
** A nicer, generic, labeling infrastructure might be nice
** This would make it easier to play with things like topology labeling (1-pi, 2-pi, etc.)
** Probably a tiny effect... but enhancing the actual effect might be good for regularization
** Can we do this with dropout? Does Caffe let you do it in a sensible way for input layers?
** Another good regularization technique
* Attenuation slopes, shifts
** Can we just slip in shifted samples?
** Or, is there a fancy way to make a custom layer which does this randomly for each training example?