A Differentiable Programming System to Bridge Machine Learning and Scientific Computing · HackerPapers discussions

A Differentiable Programming System to Bridge Machine Learning and Scientific Computing — mentioned by ChrisRackauckas

Sat, 20 Jun 2020 04:06:51 GMT

Scientific machine learning. The ability to mix scientific knowledge with machine learning in order to allow it to extrapolate accurately from small amounts of data. As machine learning tries to move towards areas where large-scale data gathering is prohibitively expensive or even unethical (like in clinical trials: you can't have a million failures to train a neural network!), mixing scientific models with machine learning will be required to bring AI into these domains in a way that's beyond hype. Physics-informed neural networks [1], differentiable programming [2], universal differential…

Paper and discussion history. A mention is not an endorsement.

A Differentiable Programming System to Bridge Machine Learning and Scientific Computing — mentioned by eggy

Sat, 11 Apr 2020 18:10:17 GMT

I think someone has translated this to J, but I am trying on my own to practice my J-fu by implementing it in my own way. Then I usually open it up to the J experts on the mailing list, and my learning takes off. There are some awesomely smart people there who are generous with their time. Yes, the takeaway is that with APL or J is that you can see the mechanics in a paragraph of code, and it is not a very trivial example. If the libraries or verbs are created to deal with some of the speed or efficiency issues, it is promising as a way of understanding the concept better. The dataframes of R…

Paper and discussion history. A mention is not an endorsement.

A Differentiable Programming System to Bridge Machine Learning and Scientific Computing — mentioned by ChrisRackauckas

Mon, 23 Mar 2020 16:47:19 GMT

The Julia Lab (MIT) | DevOps Engineer (+ More) | Onsite or Remote | Full Time | https://julia.mit.edu/ The Julia Lab is a lab in MIT's CSAIL which focuses on the development of software tooling for solving large-scale scientific problems. Named after it's original result, the Julia programming language, the Julia Lab's research ranges from scientific machine learning (SciML) [1] and differentiable programming [2] to large-scale heterogeneous parallel computing [3]. The lab is currently growing in response to new directions and collaborations, and we are looking for both researchers and devops…

Paper and discussion history. A mention is not an endorsement.

A Differentiable Programming System to Bridge Machine Learning and Scientific Computing — mentioned by ChrisRackauckas

Wed, 01 Jan 2020 04:17:50 GMT

Possibly. On this topic (machine learning, differentiable programming, GPU and parallel computing) I'd recommend the following videos: https://youtu.be/FGfx8CQHdQA https://youtu.be/OcUXjk7DFvU https://arxiv.org/abs/1907.07587 https://youtu.be/7Yq1UyncDNc https://youtu.be/_E2zEzNEy-8 https://youtu.be/6ntJ_al4oXA https://youtu.be/HfiRnfKxI64

Paper and discussion history. A mention is not an endorsement.

A Differentiable Programming System to Bridge Machine Learning and Scientific Computing — mentioned by uoaei

Wed, 13 Nov 2019 08:15:48 GMT

It's already been done by another group of researchers.[1] I'm not sure if that was a factor in their choice of Swift. [1] https://arxiv.org/abs/1907.07587

Paper and discussion history. A mention is not an endorsement.

A Differentiable Programming System to Bridge Machine Learning and Scientific Computing — mentioned by carapace

Thu, 10 Oct 2019 20:22:34 GMT

"A Differentiable Programming System to Bridge Machine Learning and Scientific Computing" https://arxiv.org/abs/1907.07587 https://news.ycombinator.com/item?id=20477873 From the abstract: > We describe Zygote, a Differentiable Programming system that is able to take gradients of general program structures. We implement this system in the Julia programming language. Our system supports almost all language constructs (control flow, recursion, mutation, etc.) and compiles high-performance code without requiring any user intervention or refactoring to stage computations. Just linking to this for…

Paper and discussion history. A mention is not an endorsement.

A Differentiable Programming System to Bridge Machine Learning and Scientific Computing — mentioned by ChrisRackauckas

Sun, 04 Aug 2019 16:27:39 GMT

What are you not sure of with Julia here? There are places in the ecosystem to not be sure of, but this isn't one. Julia has probably the most advanced mathematical optimization right now with JuMP (http://www.juliaopt.org/JuMP.jl/v0.19.2/) and some of the most advanced post-DL machine learning with the full language differentiable programming tools (Zygote, Tracker, ForwardDiff) which have showcased applications like quantum machine learning and neural stochastic differential equations (https://arxiv.org/abs/1907.07587). Some of it is still in flux, but in terms of ecosystem there's a lot of…

Paper and discussion history. A mention is not an endorsement.

A Differentiable Programming System to Bridge Machine Learning and Scientific Computing — HN submission

Fri, 19 Jul 2019 11:58:43 GMT

A Differentiable Programming System to Bridge ML and Scientific Computing

381 points; 71 discussion replies on HN. Paper and source history. A submission is not an endorsement.