Summary of A Novel Explanation Against Linear Neural Networks, by Anish Lakkapragada
A Novel Explanation Against Linear Neural Networks
by Anish Lakkapragada
First submitted to arxiv on: 30 Dec 2023
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 challenges a long-standing assumption about neural networks without activation functions, also known as linear neural networks (LNNs). The conventional wisdom is that these networks are only capable of modeling linear relationships because they lack the non-linearity introduced by activation functions. However, this paper proposes an alternative explanation: LNNs actually perform worse than linear regression in both training and testing due to their increased complexity. With more parameters, LNNs become harder to optimize, requiring more iterations to converge. The authors analyze the optimization process of LNNs and conduct rigorous tests on synthetic, noisy datasets to support this hypothesis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that neural networks without activation functions, or linear neural networks (LNNs), are not as good as people thought. Instead of only being able to model lines, LNNs actually do worse than a simple linear regression. This is because they have more parameters, making it harder for them to find the best solution. The authors looked at how well these networks work and tested them on fake data with noise to prove their point. |
Keywords
* Artificial intelligence * Linear regression * Optimization
