Summary of Approximated Likelihood Ratio: a Forward-only and Parallel Framework For Boosting Neural Network Training, by Zeliang Zhang et al.
Approximated Likelihood Ratio: A Forward-Only and Parallel Framework for Boosting Neural Network Training
by Zeliang Zhang, Jinyang Jiang, Zhuo Liu, Susan Liang, Yijie Peng, Chenliang Xu
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 to gradient estimation in neural networks is proposed, aiming to alleviate computational and memory demands. The likelihood ratio (LR) method offers a promising strategy but is constrained by significant memory consumption. To address this issue, an approximation technique is introduced to reduce computational complexity and memory requirements. By exploiting parallelism during the backward pass using LR, a high-performance training strategy is developed, which pipelines both forward and backward passes for efficient computation on specialized hardware. Experimental results demonstrate the effectiveness of the approximation technique in neural network training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to train neural networks that’s more efficient and easier to use. It uses an old method called likelihood ratio (LR) but makes it faster and uses less memory. This lets us train bigger and better neural networks, which is important because they’re used in many things like self-driving cars and recognizing faces. |
Keywords
* Artificial intelligence * Likelihood * Neural network
