[D] Eliminating "useless" variables in deep neural networks? • r/MachineLearning

In a very deep network such as a conv net with lots of filters and/or full layers, it seems to me that not all filters/weights are equally important - some could even be useless. Is there a scheme that removes these from the network (to save computation time)? I'm not talking about dropout which temporarily takes them out, I mean permanently take them out based on small gradients, high varience, etc. If anyone knows anything I'd appreciate some links/papers (sorry I'm new to NN).