Summary of Accelerating the Low-rank Decomposed Models, by Habib Hajimolahoseini et al.
Accelerating the Low-Rank Decomposed Models
by Habib Hajimolahoseini, Walid Ahmed, Austin Wen, Yang Liu
First submitted to arxiv on: 24 Jul 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 Tensor decomposition is a mathematical approach for compressing data by applying Low Rank Decomposition techniques on tensors or matrices. Although it can significantly reduce redundancy, it’s not commonly used in AI models due to the addition of new layers, which increases model depth and training/inference latency. This paper explores modifying low-rank decomposition techniques to balance high accuracy with low memory consumption and faster training/inference for AI models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to shrink big datasets without losing important information. It’s called tensor decomposition, and it can make data take up less space on computers. The problem is that when we use this method, our models might become too deep and slow down training or using the model. In this study, researchers are trying to find a solution that lets us have both small datasets and fast training/inference. |
Keywords
* Artificial intelligence * Inference
