Plots for Conference about Miscellaneous topics of PPTS
deep neural networks with domain adaptation
Discussed at the PPTS meeting on 4th April 2016, contribution
| Plot | Description |
|---|---|
 .C .png .pdf |
A proof of principle that caffe trained deep neural networks do the job of signal/background separation, in the ballpark of what we're used to from TMVA. This shall demonstrate that the caffe test setup works in principle and we can assume trainings have converged and we can go on to the next step |
 .C .png .pdf |
A by-eye comparison of networks with and without domain adaptation. The signal to background is hardly distinguishable, the network without domain adaptation is even a bit better. |
| I | Attachment | History | Action | Size | Date | Who | Comment |
|---|---|---|---|---|---|---|---|
 c |
adding_grl_to_network.C | r1 | manage | 48.8 K | 2016-04-08 - 10:34 | PaulSeyfert | plots for deep learning with domain adaptation |
 pdf |
adding_grl_to_network.pdf | r1 | manage | 36.9 K | 2016-04-08 - 10:34 | PaulSeyfert | plots for deep learning with domain adaptation |
 png |
adding_grl_to_network.png | r1 | manage | 18.2 K | 2016-04-08 - 10:34 | PaulSeyfert | plots for deep learning with domain adaptation |
| c |
caffe_vs_tmva.C | r1 | manage | 49.0 K | 2016-04-08 - 10:34 | PaulSeyfert | plots for deep learning with domain adaptation |
| pdf |
caffe_vs_tmva.pdf | r1 | manage | 36.8 K | 2016-04-08 - 10:34 | PaulSeyfert | plots for deep learning with domain adaptation |
| png |
caffe_vs_tmva.png | r1 | manage | 16.8 K | 2016-04-08 - 10:34 | PaulSeyfert | plots for deep learning with domain adaptation |
Edit | Attach | Topic revision: r2 - 2016-04-08 - PaulSeyfert
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