Summary of A Tensor Decomposition Perspective on Second-order Rnns, by Maude Lizaire et al.
A Tensor Decomposition Perspective on Second-order RNNs
by Maude Lizaire, Michael Rizvi-Martel, Marawan Gamal Abdel Hameed, Guillaume Rabusseau
First submitted to arxiv on: 7 Jun 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 The abstract discusses Second-order Recurrent Neural Networks (2RNNs), which have connections to formal language theory. However, their large parameter tensor makes computations intractable. To address this issue, researchers proposed methods like MIRNN or tensor decomposition. This work studies a 2RNN model parameterized using the CP decomposition, called CPRNN. The authors analyze how rank and hidden size affect model capacity and show relationships between RNNs, 2RNNs, MIRNNs, and CPRNNs. Empirically, CPRNNs outperform other models on the Penn Treebank dataset with optimal choices of rank and hidden size. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new type of neural network called CPRNN. It’s an extension to regular neural networks that helps them understand sequences better. The problem is that these networks have too many parameters, which makes it hard for computers to do the math. To solve this, researchers came up with ways to reduce the number of parameters. This paper explores one of those methods and shows how it works compared to other types of neural networks. They tested CPRNN on a language dataset and found that when done right, it can be better than the other models. |
Keywords
» Artificial intelligence » Neural network
