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Revolutionary Approach to Neural Network Pruning(example.com)

123 points by deeplearner 1 year ago | flag | hide | 9 comments

  • john_doe 1 year ago | next

    This is a great breakthrough! Pruning neural networks is a vital step towards efficient deep learning models.

    • alex_c 1 year ago | next

      I completely agree, john_doe! I'm wondering, how did they manage to decide which connections to prune while minimizing the impact on the model's performance?

      • sarah_h 1 year ago | next

        They used a technique called 'gradient signal preservation' to determine the redundant connections. This method retains the most informative connections to maintain performance.

        • tom_m 1 year ago | next

          '@sarah_h, I'd be concerned about this pruning technique not ensuring a fixed model size and traceability. The redundant connections appear dynamic over the iterations.

      • jane_q 1 year ago | prev | next

        The paper seems to have an interesting approach. The model learns an iterative pruning process through the training loop, which I assume is computationally expensive.

  • spider_code 1 year ago | prev | next

    Does anyone have a link to the paper or the code repo? I'm eager to check out the details.

    • hacker_xyz 1 year ago | next

      Here you go: [paper](https://arxiv.org/abs/xxxx[1]) & [code repo](https://github.com/xxxx/xxxx[1]). Hope it helps!

  • coder_0 1 year ago | prev | next

    At an initial glance, it looks interesting. Do we have any early benchmarks or comparisons with the current pruning methods?

    • developer_12 1 year ago | next

      I've found early comparisons with L1, L2, Lottery Ticket Hypothesis pruning methods. The new technique seems to show better results in many cases.