Hi all, I'm implementing a hierarchical CLA by connecting the regions (implemented in ROS).
The easiest way seems to be to run multiple OPF clients and chain output of one to input of some other. Recently we got PassThru encoder merged in (thanks Keithcom!) so this part is ok. My question is, can I make an OPF model that uses some encoder (to transform real world data) but outputs an SDR? Eg., I want to encode letters as SDRCathegory (see linguist project), but output a SDR so I can pipe it to other models with PassThru enc, and at the end of the pipeline again feed in a SDR but this time decode back to character. Btw NLP sidestep: I was playing with the linguist project for text generation, and I wonder why the performance is quite bad (compared to the state-of-the art - deep NN) and I attribute it to the lack of abstraction at higher levels of hierarchy - hence the ROS hierarchy! Do you have an input how to increase performance there? Thanks for your help, Cheers, ~breznak -- Marek Otahal :o)
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