But in reality, sparse NN is just loose it's performance, mean loose precision and recall. Precision, means, larger probability of errors; recall - if you work with piece of information, which could consist of few, ie predicates, it will see not all predicates.
To be concrete, for good trained full-scale NN, usually considered 70-90% for precision and for recall; but if use small fraction of weights, usually will got drop of performance to about 40-70%, which is good enough for many cases, considering saves on size and computations.
But in reality, sparse NN is just loose it's performance, mean loose precision and recall. Precision, means, larger probability of errors; recall - if you work with piece of information, which could consist of few, ie predicates, it will see not all predicates.
To be concrete, for good trained full-scale NN, usually considered 70-90% for precision and for recall; but if use small fraction of weights, usually will got drop of performance to about 40-70%, which is good enough for many cases, considering saves on size and computations.