Summary of Computability Of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility Through Quantization, by Holger Boche and Vit Fojtik and Adalbert Fono and Gitta Kutyniok
Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization
by Holger Boche, Vit Fojtik, Adalbert Fono, Gitta Kutyniok
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Complexity (cs.CC)
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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 paper explores the limitations of deep learning methods in various application fields, particularly in safety-critical and high-responsibility applications requiring stricter performance guarantees. Researchers have found theoretical limitations on computability, undermining the feasibility of performance guarantees when employed on real-world computers. The study examines computability from two perspectives: classification problems and training neural networks. Findings include restrictions on algorithmic solvability of classification problems and infeasible algorithmic detection of failure in computations. Additionally, algorithmic limitations are proven in training deep neural networks, even with well-behaved underlying problems. However, the study shows that quantized versions of classification and deep network training can overcome these computability restrictions to a certain degree. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning has been super successful, but it’s not perfect. One problem is that it’s not always trustworthy, which is important in some applications where things need to be really reliable. Scientists have found that deep learning can’t do everything we want it to do because of limitations on how computers work. This paper looks at those limitations and finds out what they mean for using deep learning in different situations. It shows that some types of problems are hard or impossible to solve, even if the problem itself is easy. However, the study also suggests that a special kind of “quantized” version of deep learning might be able to overcome some of these limitations. |
Keywords
» Artificial intelligence » Classification » Deep learning
