Summary of Deep Learning with Parametric Lenses, by Geoffrey S. H. Cruttwell et al.
Deep Learning with Parametric Lenses
by Geoffrey S. H. Cruttwell, Bruno Gavranovic, Neil Ghani, Paul Wilson, Fabio Zanasi
First submitted to arxiv on: 30 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Logic in Computer Science (cs.LO)
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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 A novel categorical semantics for machine learning algorithms is proposed, using lenses, parametric maps, and reverse derivative categories to unify various gradient descent methods, loss functions, and architectures under a single explanatory framework. This framework encompasses ADAM, AdaGrad, Nesterov momentum, MSE, Softmax cross-entropy, and more, shedding light on their similarities and differences. The approach can be applied to both continuous and discrete domains, including Boolean and polynomial circuits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning algorithms get a new explanation! Researchers are trying to understand how different algorithms work together. They’re using special categories called lenses, maps, and reverse derivatives to connect lots of different methods. This helps us see how some algorithms are similar, while others are very different. It even works for computers that only use 0s and 1s! |
Keywords
» Artificial intelligence » Cross entropy » Gradient descent » Machine learning » Mse » Semantics » Softmax
