Summary of The Elements Of Differentiable Programming, by Mathieu Blondel et al.
The Elements of Differentiable Programming
by Mathieu Blondel, Vincent Roulet
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Programming Languages (cs.PL)
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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 emerging paradigm of differentiable programming, which enables end-to-end differentiation of complex computer programs and makes gradient-based optimization possible. This new approach builds upon areas like automatic differentiation, graphical models, optimization, and statistics. The book presents a comprehensive review of fundamental concepts for differentiable programming, adopting two main perspectives: optimization and probability. Differentiable programming is not just about differentiating programs but also designing them with differentiation in mind, introducing probability distributions over execution to quantify uncertainty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Differentiable programming allows us to make complex computer programs “differentiable”, which means we can optimize their performance using machine learning techniques. This idea combines concepts from computer science and applied mathematics to create a new way of thinking about how computers work. The book explains the basics of differentiable programming in an easy-to-understand way. |
Keywords
* Artificial intelligence * Machine learning * Optimization * Probability
