Approach
HashedNets uses a low-cost hash function to randomly group connection weights into hash buckets, and all connections within the same hash bucket share a single parameter value. These parameters are tuned to adjust to the HashedNets weight sharing architecture with standard backprop during training.
Experiment
References:
Compressing Neural Networks with the Hashing Trick, Wenlin Chen, 2015, ICML
