I’m wondering if I can do the followoing thing:
Basically I want to build a deep learning model which takes a pre-computed set of features which already encodes some knowledge on the target, and concatenates it along a learnable vector of parameters and train a neural network-based architecture which then jointly trains these embeddings concatenated to the fixed feature vector and returns a prediction.
I just have a perplexity: should I pretrain the embeddings before and using them after by concatenating with features that I already have, or can I train the embeddings jointly with the overall model?
To summarize, ny idea is the following: I want to use both the information provided by tabular features that I already have, plus I want to learn the best representations directly from data.