Routing the Lottery: When Pruning Learns to Choose
A model can be small and still be badly organized. That is the quiet problem behind a lot of model compression work. We often ask whether a neural network can be pruned without losing too much accuracy. Fair enough. Budgets are real. Memory is not decorative. But the question hides a stronger assumption: that one sparse structure should serve every input equally well. ...