Summary of Cyclic Sparse Training: Is It Enough?, by Advait Gadhikar et al.
Cyclic Sparse Training: Is it Enough?
by Advait Gadhikar, Sree Harsha Nelaturu, Rebekka Burkholz
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach in iterative pruning for achieving state-of-the-art sparse networks is proposed, challenging the common hypothesis that improved mask identification and implicit regularization are key factors. Instead, repeated cyclic training schedules enable improved optimization, which is verified by showing that pruning at initialization can outperform standard iterative pruning methods. The dominant mechanism behind this improvement is attributed to a better exploration of the loss landscape leading to lower training losses. However, high sparsity requires a strong coupling between learnt parameter initialization and mask, which is achieved through SCULPT-ing (repeated cyclic training followed by a single pruning step), matching state-of-the-art performance at reduced computational cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Pruning networks to make them faster and more efficient has become a popular technique in machine learning. Usually, this involves identifying the most important parts of the network and removing the rest. But why does this work? Some researchers think it’s because the process helps identify the most important parts of the network, while others believe that it allows for better optimization. A new study sheds light on this question by showing that pruning at the beginning of training can actually lead to better results than the traditional iterative approach. The key is to use a special type of training called cyclic training, which allows the model to explore different parts of the loss landscape and find the optimal solution more quickly. |
Keywords
» Artificial intelligence » Machine learning » Mask » Optimization » Pruning » Regularization
