Propose an architectural modification to the original neural auto-regressive network that enables it to effectively model complex interactions between attributes of different types (continuous and discrete). Justify your choice with theoretical insights or empirical evidence, and discuss any tradeoffs involved.

Modify the original neural auto-regressive network by introducing a parameter sharing scheme similar to NADE. This will allow the model to capture complex interactions between attributes while maintaining real-time inference capabilities at the cost of increased computational complexity due to shared parameters.