Media Summary: Friday Talks - 20260320 Speaker: A. René Geist Title: Talk from HSF/IRIS-HEP Analysis Ecosystem 2 Workshop ( For more information about Stanford's Artificial Intelligence professional and graduate programs visit:

Softjax Softtorch Soft Differentiable Programming - Detailed Analysis & Overview

Friday Talks - 20260320 Speaker: A. René Geist Title: Talk from HSF/IRIS-HEP Analysis Ecosystem 2 Workshop ( For more information about Stanford's Artificial Intelligence professional and graduate programs visit: Presenter: Gordon Plotkin Presented at POPL'2020. Supplementary video for our Robotics: Science and Systems (RSS) 2022 paper, "Certifiable Robot Design Optimization using ... Yet another example from my demonstrative project on

Dimitri spittoon it is and Simon Peter Jones on Thank you welcome to the real world so my name is Dan Zeng today I'll be presenting demystifying 2022 LLVM Developers' Meeting ------ LAGrad: Leveraging the MLIR Ecosystem for Efficient ... --- Mathematical derivatives are vital components of many computing ... Scientific computing is increasingly incorporating the advancements in machine learning and the ability to work with large ... According to Max Haughton, the calculation of gradients is a way to understand the universe. For the entire history of computing, ...

A presentation on back-propagation and automatic differentiation, and demonstration of how this method is used for calibration in ... Video from Compiler Research / IRIS-HEP Mini-Workshop: In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course.

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SoftJAX & SoftTorch: Soft Differentiable Programming for Scientific Machine Learning - [@AndReGeist]
Differentiable Programming in HEP
Machine Learning 10 - Differentiable Programming | Stanford CS221: AI (Autumn 2021)
A Simple Differentiable Programming Language
Certifiable Robot Design Optimization using Differentiable Programming
Differentiable programming in action
Efficient Differentiable Programming in a Functional Array Processing Language
Demystifying Differentiable Programming - Shift/Reset the Penultimate Backpropagator
2022 LLVM Dev Mtg: LAGrad: Leveraging the MLIR Ecosystem for Efficient Differentiable Programming
Differentiable Programming in C++ - Vassil Vassilev & William Moses - CppCon 2021
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SoftJAX & SoftTorch: Soft Differentiable Programming for Scientific Machine Learning - [@AndReGeist]

SoftJAX & SoftTorch: Soft Differentiable Programming for Scientific Machine Learning - [@AndReGeist]

Friday Talks - 20260320 https://fridaytalks.github.io Speaker: A. René Geist https://andregeist.github.io/ Title:

Differentiable Programming in HEP

Differentiable Programming in HEP

Talk from HSF/IRIS-HEP Analysis Ecosystem 2 Workshop (https://indico.cern.ch/event/1125222/).

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Machine Learning 10 - Differentiable Programming | Stanford CS221: AI (Autumn 2021)

Machine Learning 10 - Differentiable Programming | Stanford CS221: AI (Autumn 2021)

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai ...

A Simple Differentiable Programming Language

A Simple Differentiable Programming Language

Presenter: Gordon Plotkin Presented at POPL'2020.

Certifiable Robot Design Optimization using Differentiable Programming

Certifiable Robot Design Optimization using Differentiable Programming

Supplementary video for our Robotics: Science and Systems (RSS) 2022 paper, "Certifiable Robot Design Optimization using ...

Sponsored
Differentiable programming in action

Differentiable programming in action

Yet another example from my demonstrative project on

Efficient Differentiable Programming in a Functional Array Processing Language

Efficient Differentiable Programming in a Functional Array Processing Language

Dimitri spittoon it is and Simon Peter Jones on

Demystifying Differentiable Programming - Shift/Reset the Penultimate Backpropagator

Demystifying Differentiable Programming - Shift/Reset the Penultimate Backpropagator

Thank you welcome to the real world so my name is Dan Zeng today I'll be presenting demystifying

2022 LLVM Dev Mtg: LAGrad: Leveraging the MLIR Ecosystem for Efficient Differentiable Programming

2022 LLVM Dev Mtg: LAGrad: Leveraging the MLIR Ecosystem for Efficient Differentiable Programming

2022 LLVM Developers' Meeting https://llvm.org/devmtg/2022-11/ ------ LAGrad: Leveraging the MLIR Ecosystem for Efficient ...

Differentiable Programming in C++ - Vassil Vassilev & William Moses - CppCon 2021

Differentiable Programming in C++ - Vassil Vassilev & William Moses - CppCon 2021

https://cppcon.org/ https://github.com/CppCon/CppCon2021 --- Mathematical derivatives are vital components of many computing ...

Frank Schäfer - Differentiable Programming for Quantum Control with SciML

Frank Schäfer - Differentiable Programming for Quantum Control with SciML

Differentiable Programming

Models as Code: Differentiable Programming with Zygote

Models as Code: Differentiable Programming with Zygote

Scientific computing is increasingly incorporating the advancements in machine learning and the ability to work with large ...

Intro of Differentiable Programming - FutureAI 1

Intro of Differentiable Programming - FutureAI 1

[Slide] Future AI -

DConf Online '22 - Differentiable Programming in D

DConf Online '22 - Differentiable Programming in D

According to Max Haughton, the calculation of gradients is a way to understand the universe. For the entire history of computing, ...

2024 10 10 OMUG Meeting Differentiable Programming

2024 10 10 OMUG Meeting Differentiable Programming

A presentation on back-propagation and automatic differentiation, and demonstration of how this method is used for calibration in ...

Post-Optimization Automatic Differentiation by Synthesizing LLVM (William Moses, MIT)

Post-Optimization Automatic Differentiation by Synthesizing LLVM (William Moses, MIT)

Video from Compiler Research / IRIS-HEP Mini-Workshop:

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.

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