Summary of Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks, by Jialin Zhao et al.
Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks
by Jialin Zhao, Yingtao Zhang, Xinghang Li, Huaping Liu, Carlo Vittorio Cannistraci
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes a novel training methodology called Sparse Spectral Training (SST) to address the growing computational demands posed by increasing parameters in neural networks. The method updates all singular values and selectively updates singular vectors of network weights, optimizing resource usage while closely approximating full-rank training. SST refines the training process by employing a targeted updating strategy for singular vectors, determined by a multinomial sampling method weighted by the significance of the singular values. The approach is tested on both Euclidean and hyperbolic neural networks across various tasks, including natural language generation, machine translation, node classification, and link prediction, demonstrating its capability to outperform existing memory reduction training methods and comparable performance with full-rank training in some cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to train neural networks that uses less memory. Currently, big neural networks need lots of computer power and memory to work well. But what if we could make them use less memory while still performing just as well? That’s exactly what the authors of this paper did. They developed an algorithm called Sparse Spectral Training (SST) that updates some parts of the network more than others, making it much faster and more efficient. The researchers tested SST on many different types of neural networks and tasks, including natural language processing and machine learning, and found that it worked just as well or even better than traditional methods. |
Keywords
» Artificial intelligence » Classification » Machine learning » Natural language processing » Translation
