My concern is that since the training process is noisy (RL) the optimization could take more time to "isolate" those features, and maybe some smarter model architecture could bias the algorithm in giving more importance to them from the beginning
They are available at the same time. Imagine that the input is a 251-dimensional vector where the first 200 values are related to some feature A, the next 5 to feature B, and the last value to feature C. But features B and C are very important for the prediction
fedetask OP t1_iuduy9k wrote
Reply to comment by eigenham in [R] Deep model with inputs of unbalanced sizes by fedetask
My concern is that since the training process is noisy (RL) the optimization could take more time to "isolate" those features, and maybe some smarter model architecture could bias the algorithm in giving more importance to them from the beginning