Summary of Random Search As a Baseline For Sparse Neural Network Architecture Search, by Rezsa Farahani
Random Search as a Baseline for Sparse Neural Network Architecture Search
by Rezsa Farahani
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 The proposed research introduces a novel approach to learning high-performing sparse neural networks. By applying the Random Search algorithm, researchers aim to find better initialized sparse sub-networks positioned advantageously in the loss landscape. The study compares the post-training performances of found sparse networks at various levels of sparsity against their fully connected parent networks and random sparse configurations. The results show that a significant level of performance can be preserved even when the network is highly sparse, but also highlight that initialized sparse networks found by Random Search do not perform better or converge more efficiently than random counterparts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores ways to create efficient and effective neural networks while reducing computational complexity. By applying the Random Search algorithm, scientists aim to find the best sparse configurations for their models. The study compares how well these models perform compared to their fully connected versions and randomly generated configurations. Surprisingly, even when using a highly sparse network, the model can still achieve impressive results. |
