500 points by ml_genius 1 year ago flag hide 17 comments
ml_enthusiast 1 year ago next
This is a revolutionary breakthrough in machine learning! Training models in sub-quadratic time? That's incredible!
original_poster 1 year ago next
Thanks! It took our team years to achieve this, and still can't believe it ourselves.
ai_engineer 1 year ago prev next
I am impressed. Any insights on how this works and how we can apply it to our current models?
ml_enthusiast 1 year ago prev next
I've read the paper, and it's quite complex. Essentially, they used a new algorithm to optimize matrix multiplication. They claim it works specifically well with Convolutional Neural Networks (CNNs) and Transformers.
ai_engineer 1 year ago next
Very interesting! Will try out their proposed optimization techniques on our CNNs and share the results with the community.
curious_dev 1 year ago prev next
Does this mean, training times for large models will reduce significantly? Are there any computational downsides or challenges while implementing this?
ml_enthusiast 1 year ago next
Yes, the goal is to reduce training times significantly. As for the computational downsides, we have not found any for large models yet. But there might be some challenges for smaller models, as the optimization technique may not have as much impact there.
deep_learning_guru 1 year ago prev next
This innovation could revolutionize the way we train and use machine learning models. Kudos to your team!
original_poster 1 year ago next
@deep_learning_guru, thanks a lot for the encouragement. We're still working on getting everything working smoothly, and your support is highly appreciated.
another_commenter 1 year ago prev next
Amazing, I can't wait to test this out! Are there any potential downsides with getting the production-ready models using this technique?
ml_enthusiast 1 year ago next
Not at this point, but there's a lot of room for research on optimizing this implementation for the production of large-scale models.
critical_thinker 1 year ago prev next
Though this is exciting, to what degree does this reduce training time & energy consumption in real-world applications?
original_poster 1 year ago next
Right now, we see an average reduction of 30-40% in training time across various models. We believe further improvements are possible as we refine the algorithm.
research_partner 1 year ago prev next
Our next step is to apply this to reinforcement learning models and see if the benefits hold true.
deep_learning_guru 1 year ago next
@research_partner, that's an exciting step indeed. More improvements to come!
algos_interest 1 year ago prev next
What ELSE can we expect in the future for ML time and energy optimization? This is a giant leap!
original_poster 1 year ago next
We're sure that more exciting breakthroughs are on the horizon as many researchers are working in this area.