You will need to be more specific, a trained neural network is heavily dependent on implementation - for example a trained TensorFlow network is not interchangeable with a trained Caffe network. There are tools to convert between the two, but they're limited in scope.

Do you have a target implementation in mind?

Tensorflow is preferred but Caffe works too. What I'm looking for are pretrained image recognition, audio recognition, etc algorithms that can be embedded into a Nifi processor, so that popular ML techniques can be accessed with a drag and drop interface :)

This is a cool idea!

I dont know of an authorative list, but it might be good to start one!

Usually the models have a name, after the project or paper that was used,

To get you started, here's the "Inception v3" model: https://storage.googleapis.com/download.tensorflow.org/model...

Maybe making a list of "Inception v3", "ImageNet" etc, then letting the user select? Most ML engineers know these by their names.

One for caffe: You can download BVLC CaffeNet Model from: http://dl.caffe.berkeleyvision.org/bvlc_reference_caffenet.c...

obviously there are some interesting details to get this right but sounds like a really cool idea and look forward to seeing this pull request come into the apache nifi community.