150 points by deeplearner 1 year ago flag hide 20 comments
deeplearner 1 year ago next
This is an interesting approach to neural network pruning. It reminds me of the one we tried last year but with a more elegant formulation. I hope it gets more attention.
ai_enthusiast 1 year ago next
Agreed, this could indeed be a game-changer in neural network pruning. The authors' experiments look promising.
ai_enthusiast 1 year ago next
Exactly, the unique combination of techniques could lead to better performance and reduced computational complexity.
quantum_computing 1 year ago prev next
Compressing neural network models is essential to enable deployment on edge devices. I hope this work encourages more innovations in this area.
embedded_engineer 1 year ago next
True, edge devices benefit from small models that don't sacrifice performance, and lighter models are less costly in energy consumption as well.
hobbyist 1 year ago prev next
That's a good discussion to follow. I'll also check out the mentioned resources.
anotheruser 1 year ago prev next
Can anyone point me towards relevant open-source implementations of this technique?
deeplearner 1 year ago next
There are a few GitHub repos with working implementations; some are in PyTorch, and others are in TensorFlow. I'll post some links below.
opensource_lover 1 year ago prev next
Sharing a link to a repo that I recently came across that discusses various pruning techniques, including this new approach. Check it out: [github.com/...
anotheruser 1 year ago next
Thanks for adding the additional resource! I'm curious to see what the researcher community will come up with for faster pruning techniques.
tooldeveloper 1 year ago prev next
I wonder if the same method could be used to prune other machine learning models such as SVMs and Random Forests.
ml_fan 1 year ago next
It's definitely worth a try for some models, although tree-based models typically have a different algorithm for pruning. It's interesting to see if connections could be made.
statistician 1 year ago prev next
Perhaps looking into the data distribution could offer more insights for pruning tree-based algorithms, similar to this work's notion of sparsifying neural networks.
cs_professor 1 year ago prev next
I'm considering adding this approach to next semester's deep learning course at my university. The paper's complexity analysis and numerical examples make it more accessible for undergrad students.
cs_student31 1 year ago next
That would be awesome, Prof. The description in the paper is quite easy to follow. I'm sure students will benefit from learning it!
researcher 1 year ago prev next
Really enjoyed reading the paper; it was well-written. I'm looking forward to seeing how this technique stands against other currently popular methods in follow-up research!
new_hire 1 year ago prev next
We should look into applying this technique at the company I started working at last month. It might help improve our machine learning models' performance and reduce computational costs.
educator 1 year ago prev next
I like the idea of discussing this paper with my high school students; I hope it'll spark their interest in AI research and development.
hardware_engineer 1 year ago prev next
Are there any implementations or surveys connecting this method to specific hardware accelerators for neural networks? This would be a crucial step for real-world impact.
stats_guru 1 year ago prev next
It's nice to see academic research pursuing methods that can have real-world implications for machine learning, AI, and technology in general.