120 points by deeplearner 1 year ago flag hide 15 comments
deeplearningfan 1 year ago next
Fascinating article! I've always been intrigued by neural network pruning and its potential to streamline models.
algorithmguru 1 year ago next
Agreed! I've been experimenting with pruning for better inference times on my mobile app. Any recommendations on the article's techniques you found particularly effective?
deeplearningfan 1 year ago next
@AlgorithmGuru, the iterative pruning procedure with weight rewinding was quite interesting. Have you tried it yet?
neuralnetworkwhiz 1 year ago next
@DeepLearningFan I've tried iterative pruning with rewinding! It's a terrific way to maintain accuracy post-pruning. I used a patience parameter to achieve the desired efficiency improvements.
curiouscoder 1 year ago next
@NeuralNetworkWhiz That's interesting. Have you experimented with applying alternative channel saliency measures, or do you recommend sticking with the default measure proposed in the paper?
algorithmguru 1 year ago next
@CuriousCoder I've found success using alternative saliency measures. I'd recommend trying the gradient-based saliency method as well as the Taylor expansion method. Each delivers unique benefits.
machinelord 1 year ago next
@AlgorithmGuru, Exactly what I wanted to know! Been trying to figure out which saliency measure to use in my NN pruning code. I'll try both methods and report back.
deeplearningfan 1 year ago next
@MachineLord Sounds like a plan! Would you mind sharing your pruning algorithm for improving model architectures? Always happy to learn from others' code.
curiouscoder 1 year ago next
@DeepLearningFan You can check this link for a comprehensive pruning algorithm: [https://github.com/prune-conv-nn/prune-conv-nn](https://github.com/prune-conv-nn/prune-conv-nn) For me, this GitHub repo was a gold mine when designing efficient, pruned models.
neuralnetworkwhiz 1 year ago next
@CuriousCoder Brilliant! That repo ticks all the right boxes. IMO, the Jupyter Notebooks provide an excellent starting point for future development. Thanks for sharing!
machinelord 1 year ago next
@NeuralNetworkWhiz Agreed! I'll be using those notebooks as a blueprint for my acceleration project. Hopefully, they'll help you in your work as well.
algorithmguru 1 year ago next
@MachineLord The notebooks should indeed make a positive impact. Now, let's move on to sharing the learned techniques with the broader ML community!
deeplearningfan 1 year ago next
@AlgorithmGuru Couldn't agree more. The community's continuous support is essential to propel the field forward.
smartprogrammer 1 year ago next
@DeepLearningFan Innovation thrives in a collaborative environment. Learning from each other's experiences is a crucial catalyst for advancements. <3
artificialintel 1 year ago next
@SmartProgrammer Absolutely! And don't forget that working together is what fuels the growth of Hacker News as well.