Context Starter: In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course. A 'new" way to compute derivatives at the machine precision with very modest overhead.

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In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course. A 'new" way to compute derivatives at the machine precision with very modest overhead.

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  • In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course.
  • A 'new" way to compute derivatives at the machine precision with very modest overhead.

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Image Reference Set

CS8850: Reverse mode AD
4 Reverse Mode Automatic Differentiation
Reverse mode algorithmic differentiation (AD)
Differentiable Programming Part 1: Reverse-Mode AD Implementation
Parallel Algorithmic Differentiation for OCaml
[SC'21] Reverse Mode Automatic Differentiation and Optimization of GPU Kernels via Enzyme
CS8850: Forward Mode AD
Reverse mode AD / backprop: explanation, Julia example, and custom rules
FHPNC 2021 - Reverse Automatic Differentiation for Accelerate (Extended Abstract)
Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation
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Check Main Notes
CS8850: Reverse mode AD

CS8850: Reverse mode AD

Read more details and related context about CS8850: Reverse mode AD.

4 Reverse Mode Automatic Differentiation

4 Reverse Mode Automatic Differentiation

Read more details and related context about 4 Reverse Mode Automatic Differentiation.

Reverse mode algorithmic differentiation (AD)

Reverse mode algorithmic differentiation (AD)

Read more details and related context about Reverse mode algorithmic differentiation (AD).

Differentiable Programming Part 1: Reverse-Mode AD Implementation

Differentiable Programming Part 1: Reverse-Mode AD Implementation

In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course.

Parallel Algorithmic Differentiation for OCaml

Parallel Algorithmic Differentiation for OCaml

Read more details and related context about Parallel Algorithmic Differentiation for OCaml.

[SC'21] Reverse Mode Automatic Differentiation and Optimization of GPU Kernels via Enzyme

[SC'21] Reverse Mode Automatic Differentiation and Optimization of GPU Kernels via Enzyme

Read more details and related context about [SC'21] Reverse Mode Automatic Differentiation and Optimization of GPU Kernels via Enzyme.

CS8850: Forward Mode AD

CS8850: Forward Mode AD

A 'new" way to compute derivatives at the machine precision with very modest overhead.

Reverse mode AD / backprop: explanation, Julia example, and custom rules

Reverse mode AD / backprop: explanation, Julia example, and custom rules

Read more details and related context about Reverse mode AD / backprop: explanation, Julia example, and custom rules.

FHPNC 2021 - Reverse Automatic Differentiation for Accelerate (Extended Abstract)

FHPNC 2021 - Reverse Automatic Differentiation for Accelerate (Extended Abstract)

Read more details and related context about FHPNC 2021 - Reverse Automatic Differentiation for Accelerate (Extended Abstract).

Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation

Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation

Read more details and related context about Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation.