#autodiff — Public Fediverse posts

Geomatic – a command-driven geometry studio enabled with autodiff

https://www.tinyvolt.com/geomatic

#HackerNews #Geomatic #autodiff #geometry #studio #commandline #tools

Geomatic – a command-driven geometry studio enabled with autodiff

https://www.tinyvolt.com/geomatic

#HackerNews #Geomatic #autodiff #geometry #studio #commandline #tools

Geomatic – a command-driven geometry studio enabled with autodiff

https://www.tinyvolt.com/geomatic

#HackerNews #Geomatic #autodiff #geometry #studio #commandline #tools

Geomatic – a command-driven geometry studio enabled with autodiff

https://www.tinyvolt.com/geomatic

#HackerNews #Geomatic #autodiff #geometry #studio #commandline #tools

Geomatic – a command-driven geometry studio enabled with autodiff

https://www.tinyvolt.com/geomatic

#HackerNews #Geomatic #autodiff #geometry #studio #commandline #tools

#TIL about dual numbers and their relation to #autodiff 🧵

You know about complex numbers, you add i such as i²=-1. Dual numbers add ε such as ε²=0. And this create a very elegant way to see autodiff 1/4

Source-to-source #autodiff for #APL: https://github.com/BobMcDear/ada

package :https://www.kernel-operations.io/rkeops/ useR! video: www.youtube.com/watch?v=5DDd... #rstats #kernels #gpu #autodiff

Kernel Operations on GPU or CP...

The worst part about having a permanent position is that you see all these interesting other jobs popping up yet can't apply - this one in #autodiff: https://jobs.inria.fr/public/classic/en/offres/2024-07696

I tried using analytical normal vector calculation on a fractal. It turns out that even without any antialiasing, the image becomes much less noisy.

#sdf #indiedev #compiler #autodiff

Got control flow working, now I can use the fractals from the distance estimator compendium:

https://jbaker.graphics/writings/DEC.html

#fractal #sdf #autodiff #render #rendering

Using the gradient length of the signed distance field, we can see where the function is non-euclidean.

#sdf #autodiff #render

Beyond Backpropagation - Higher Order, Forward and Reverse-mode Automatic Differentiation for Tensorken
https://open.substack.com/pub/getcode/p/beyond-backpropagation-higher-order

#Rust #rustlang #autodiff

Recently I've gotten really excited about #Enzyme as the future of #autodiff in #JuliaLang, in particular because it supports more language features than #Zygote (e.g. mutation, fast control flow, and preserving structural sparsity). I've started getting acquainted with its rules system, and I have some first impressions by comparison to #ChainRules. 🧵

#chatgpt explaining #autodiff in various ways. I am very impressed by this #llm, not just its knowledge, but also that it can understand subtle forms of humor in its way of explanations.

On Thursday I'll be at #NeurIPS2022 presenting a paper on our new system for #autodiff of implicit functions. A 🧵on the paper (https://arxiv.org/abs/2105.15183)

What happens when you use #autodiff and let your nonsmooth iterative algorithm goes to convergence?

With J. Bolte & E. Pauwels, we show that under a contraction assumption, the derivatives of the algorithm converge linearly!
Preprint: https://arxiv.org/abs/2206.00457

I will present this work this week at #NEURIPS2022

Next week I'll be at #NeurIPS2022 presenting a couple of papers. The first one is on #autodiff through #optimization (aka #unrolling) and its bizarre convergence properties. A 🧵 on the paper (https://arxiv.org/pdf/2209.13271.pdf) (1/9)

Another #JuliaLang #autodiff banger

https://youtu.be/DfKKTE9XU0Q

Technical Q. Anyone know how to do recursive binary checkpointing ("treeverse") over a number of steps that isn't determined until runtime? E.g. for an adaptive ODE solver.

Classically, the number of steps is assumed to be known in advance, I think.

#autodiff
#machinelearning
#honestly_I_have_no_idea_what_hashtag_to_use_for_obscure_technical_questions

With the core techniques for deriving #AutoDiff rules in that page, we can work out rules for complex functions like matrix factorizations. See for example this blog post on deriving rules for the LU decomposition: https://sethaxen.com/blog/2021/02/differentiating-the-lu-decomposition/

In my opinion*, this page from the ChainRules docs is the best intro to working out automatic differentiation rules: https://juliadiff.org/ChainRulesCore.jl/stable/maths/arrays.html

* disclaimer: I wrote it with lots of community input
#AutoDiff #JuliaLang #calculus #gradient

I just migrated from @[email protected] to this new account at fosstodon.org, so time for a reintroduction!

I'm a #MachineLearning engineer with a focus on probabilistic programming (#probprog) at @unituebingen, where I help scientists use ML for their research. In the office and out, one of my main passions is #FOSS, and I work on a number of #opensource packages, mostly in #JuliaLang :julia: with a focus on #probprog, #manifolds, and #autodiff.

#introduction

@johnryan Yeah I do #probprog in #JuliaLang, and it's great that we can use arbitrary Julia code within our models. This is because most of the language is differentiable with #autodiff and code is composable, which is not the case for most PPLs.

For #deeplearning research, Julia could come in handy for writing and transforming custom kernels without fussing with CUDA, as some posts in that thread note, but I have no experience with this.

Hello fediverse!

I'm a #machinelearning engineer with a focus on probabilistic programming (#probprog) at @unituebingen, where I help scientists use ML for their research. In the office and out, one of my main passions is #FOSS, and I work on a number of #opensource packages, mostly in #JuliaLang, with a focus on #probprog, #manifolds, and #autodiff.

I have no idea what this account will be about, but probably some combination of the above topics. 👋

#introduction