456 points by datawhiz 6 months ago flag hide 15 comments
mlwhiz 6 months ago next
Fascinating approach! I've been playing around with the beta version and the efficiency improvement is truly impressive. Excited to see where this goes.
quantdata 6 months ago next
Absolutely! The reduction in computation time has made a huge difference in our models. I'm impressed by the level of optimization achieved.
datawiz 6 months ago prev next
Have you encountered any issues while integrating this with existing infrastructure? We're hesitant due to potential compatibility problems.
mlwhiz 6 months ago next
@DataWiz, we did experience minor compatibility issues but none that couldn't be resolved with some minor adjustments. It's worth considering if the gain in efficiency is substantial.
codemaster 6 months ago prev next
Very interesting. Has anyone tested this against other optimization techniques, such as XGBoost or LightGBM?
researcher54 6 months ago next
@CodeMaster yes, direct comparison with popular optimization techniques shows that this approach results in better efficiency and performance.
optimizeguru 6 months ago prev next
How does this handling large datasets (billions of samples)? Do we see the same efficiency improvement?
mlwhiz 6 months ago next
@OptimizeGuru, efficiency improvement is still significant with large datasets but could be less pronounced due to factors outside the optimized algorithm. It's still a substantial time saver.
notastat 6 months ago prev next
Have any benchmarks been conducted against traditional gradient descent methods?
quantdata 6 months ago next
@NotAStat, benchmarks I've seen show this approach outperforms traditional gradient descent methods by a considerable margin.
supervisedguy 6 months ago prev next
Looking at the source code, the approach seems complex yet scalable. Have you considered open-sourcing a more generic version?
mlwhiz 6 months ago next
@SupervisedGuy, we've taken note of this suggestion and will look into releasing a more generic, open-source version of the approach in the near future.
neuralnetfan 6 months ago prev next
Most times, when improving ML algorithms, we incur a trade-off between performance and readability. How does this approach fair?
mlwhiz 6 months ago next
@NeuralNetFan, preserving readability was crucial in the development process. While highly efficient, the approach remains relatively interpretable and easily incorporated into existing project structures.
algoexplorer 6 months ago prev next
How does the algorithm deal with sparse , high-dimensional data often encountered in NLP tasks for instance?