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The Simple Essence of Automatic Differentiation - Conal Elliott

Conal Elliott presents his paper on automatic differentiation, deriving a simple, generalized AD algorithm from a natural specification. Varying the representation of derivatives yields forward and reverse mode algorithms that avoid the complexity and statefulness of backpropagation.

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Why HN readers recommended it

Cited as the more accessible companion to the paper, and as the most interesting treatment of AD via compiling to categories.

This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.

Video & recommendation details

Creator / channel
Microsoft Research
Topic
automatic differentiation
Format
talk
Level
advanced
HN recommenders in dataset
3
HN mentions in dataset
6
Recommendation years in dataset
2018–2023

These counts and years describe the included Hacker News records; they are not YouTube views or the video’s publication date.

From the original HN conversations

Excerpts from the recorded comments. Follow each source for the full comment and surrounding discussion.

  • The most interesting thing I've seen on AD is "The simple essence of automatic differentiation" (2018) [1]. See past discussion [2], and talk [3]. I think the main idea is that by compiling to categories and pairing up a function with its derivative, the pair …

    infogulch on Hacker News · 2022-04
    In Differentiable Programming – A Simple Introduction

  • Another paper related to AD was posted last month: The Simple Essence of Automatic Differentiation [0]. Video presentation [1]. I found it pretty awesome, just in case you hadn't seen it. [0]: https//news.ycombinator.com/item?id=18306860 [1]: [VIDEO]

    infogulch on Hacker News · 2018-11
    In Automatic Differentiation in Machine Learning: A Survey [pdf]

  • He also gave nice presentation on AD: [VIDEO] (surprised that you didn't already mention it, considering the venue ;-))

    noblethrasher on Hacker News · 2019-11
    In Differentiable Programming Manifesto

Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.