123 points by nnresearcher 11 months ago flag hide 13 comments
john_doe 11 months ago next
Fascinating article! I've been exploring the space of neural network pruning too. Have you tried using the Lottery Ticket Hypothesis for pruning?
hacker_alice 11 months ago next
Yes! I've tried the Lottery Ticket Hypothesis and it performed pretty well. Have you tried using other techniques like Magnitude-based pruning?
aj_kennedy 11 months ago next
Yes, I believe so too. Magnitude-based pruning can lead to significant reductions in model size while maintaining performance. The research community should continue to explore new techniques for pruning.
quant_engineer 11 months ago prev next
I find the concept of network pruning very interesting. I think there is still more potential to this space. What do you think?
anonymous_reader 11 months ago prev next
How do these techniques compare to manually pruning networks through trial and error?
research_nerd 11 months ago next
These automatic techniques for pruning are generally more effective than manually pruning networks. This is because they can preserve the performance of the original network while significantly reducing its size.
deep_learning_fan 11 months ago prev next
In addition, automatic pruning can save us a lot of time and effort in the trial-and-error process. It is more feasible for building large-scale systems.
sarah_codes 11 months ago prev next
While pruning can significantly reduce model size, what impact does it have on model accuracy and memory bandwidth?
code_master 11 months ago next
With pruning, we can reduce the number of parameters in the model without significantly impacting model accuracy. However, pruned networks may still require more memory bandwidth than the original network due to irregular connectivity patterns.
software_hero 11 months ago prev next
Memory bandwidth is an important factor in system performance. We should not only focus on model size reduction but also consider the impact on memory bandwidth when applying pruning techniques.
guest_1234 11 months ago prev next
Is there any work on combining pruning with quantization and knowledge distillation to further reduce model size?
ml_researcher 11 months ago next
Absolutely, combining pruning with quantization and knowledge distillation can lead to extremely small models with no significant loss in performance. There is ongoing research in this space.
optimization_guru 11 months ago prev next
We can also consider applying other optimization techniques like weights pruning, filter pruning, and even channel pruning. Each has its own tradeoffs, and we must choose suitable pruning methods based on our specific requirements.