Using partially defined features in an unified deep learning model
You can fill the B features with zeroes where they are not available, and add an extra 0/1 feature indicating whether the B features are present or not.
Then, you can fit whatever neural architecture is more suitable for the nature of the input data, e.g. dense layers with ReLU activations, LSTMs.