Summary of Towards a Categorical Foundation Of Deep Learning: a Survey, by Francesco Riccardo Crescenzi
Towards a Categorical Foundation of Deep Learning: A Survey
by Francesco Riccardo Crescenzi
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Category Theory (math.CT)
GrooveSquid.com Paper Summaries
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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 AI research paper abstract highlights the pressing need for stronger theoretical foundations in machine learning, where recent advances have been largely driven by ad hoc design choices rather than principled approaches. The lack of robust theoretical underpinnings has led to a significant “research debt” and concerns about reproducibility, as many papers fail to provide clear explanations or justifications for their methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is getting smarter really fast, but that’s also creating big problems. Right now, we don’t have solid reasons why some approaches work better than others, and many discoveries are based on trial-and-error rather than a deep understanding of how they work. This means that lots of research isn’t repeatable or reliable. |
Keywords
* Artificial intelligence * Machine learning
