melisgl

mgl

Common Lisp2017–2023

melisgl / mgl

mgl

Common Lisp machine learning library.

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8Distinct people

10Mentions

0Explicit recommenders

651GitHub stars

REPOSITORY AT A GLANCE

What to know before exploring

What it is

Common Lisp machine learning library.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

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Also in these discussions

melisgl/higgsml · linked in 2 of the same discussions
mmaul/clml · linked in 2 of the same discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year10 dated mentions

Observed mentions: 2017-03-29 01:23:31 UTC to 2023-05-15 08:22:49 UTC.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges

UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
Period	Start (UTC)	End (exclusive, UTC)	Mentions	Submissions	Comments
2017	2017-01-01 00:00:00 UTC	2018-01-01 00:00:00 UTC	1	0	1
2018	2018-01-01 00:00:00 UTC	2019-01-01 00:00:00 UTC	0	0	0
2019	2019-01-01 00:00:00 UTC	2020-01-01 00:00:00 UTC	0	0	0
2020	2020-01-01 00:00:00 UTC	2021-01-01 00:00:00 UTC	1	0	1
2021	2021-01-01 00:00:00 UTC	2022-01-01 00:00:00 UTC	1	0	1
2022	2022-01-01 00:00:00 UTC	2023-01-01 00:00:00 UTC	3	0	3
2023	2023-01-01 00:00:00 UTC	2024-01-01 00:00:00 UTC	4	2	2

What Hacker News says

10 sources

medo-bear·May 15, 2023

Please note that despite current lack of MGL-PAX (https://github.com/melisgl/mgl-pax) documentation, Boltzman Machines and Gaussian Processes are implemented and documented in the source, which is very well and cleanly written https://github.com/melisgl/mgl/blob/master/src/boltzmann-mac... https://github.com/melisgl/mgl/blob/master/src/gaussian-proc...

In MGL: Common Lisp machine learning library

oumua_don17·May 13, 2023Submission

MGL: Common Lisp machine learning library

216 points · Open discussion

vzcx·May 6, 2023

If you are interested in machine learning, check out Gabor Melis's library: https://github.com/melisgl/mgl. It's not an area I'm super familiar with, so I can't speak to it's feature set, but I believe he used it to win a machine learning competition some years ago. I don't think anyone's written a transformer or diffusion model with it, could be a fun challenge.

In Paradigms of A.I. Programming: Case Studies in Common Lisp (1991)

ducktective·Mar 2, 2023Submission

Mgl: Common Lisp machine learning library

4 points · Open discussion

medo-bear·Jul 21, 2022

although not necessarily bert or resnet the following probably has all the ingredients for what you are looking for. the author of this library is a research scientist at deepmind since 2015 https://github.com/melisgl/mgl#x-28MGL-BP-3A-40MGL-BP-20MGL-...

In New Lisp-Stat Release

Lisp-Stat·Jul 21, 2022

I think this depends on what part of the statistics universe you're working in. For example, within Lisp-Stat the statistics routines [1] were written by an econometrician working for the Austrian government (Julia folks might know him - Tamas Papp). It would not be exaggerating to say his job depending on it. These are state of the art, high performance algorithms, equal to anything available in R or Python. So, if you're doing econometrics, or something related, everything you need is already there in the tin. For machine learning, there's CLML [2], developed by NTT. This is the largest telco in Japan, equivalent to ATT in the USA. As well, there is MGL [3], used to win the Higgs Boson challenge a few years back. Both actively maintained. For linear algebra, MagicCL was mention elsewhere in the thread. My favourite is MGL-MAT [4], al

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so by the author of MGL. This supports both BLAS and CUBLAS (CUDA for GPUs) for solutions. Finally, there's the XLISP-STAT archive [5]. Prior to Luke Tierney, the author of XLISP-Stat joining the core R team, XLISP-STAT was the dominate statistical computing platform. There's heaps of stuff in the archive, most at least as good as what's in base R, that could be ported to Lisp-Stat. Common Lisp is a viable platform for statistics and machine learning. It isn't (yet) quite as well organised as R or Python, but it's all there. [1] https://github.com/Lisp-Stat/numerical-utilities/blob/master... [2] https://github.com/mmaul/clml [3] https://github.com/melisgl/mgl [4] https://github.com/melisgl/mgl-mat [5] https://lisp-stat.dev/docs/resources/xlisp/

In New Lisp-Stat Release

vindarel·May 3, 2022

If you're (very) motivated you could try MGL or CLML! https://github.com/melisgl/mgl - https://github.com/mmaul/clml - https://github.com/CodyReichert/awesome-cl#machine-learning MGL's auhor [won](https://github.com/melisgl/higgsml) the Higgs Boson ML Challenge.

In Why Hy?

medo-bear·Oct 24, 2021

my background is also in physics and mathematics. in fact i think common lisp is well suited to a physicist-hacker type mindset. i could probably write a lengthy blog post about why i think this could be, but some of the key appeal factors are: * unmatched interactive development which could be used for exploration of problems such as dynamical and complex systems * although it is a very high level language you can perform low level optimizations in common lisp itself * extremely powerful macro system that allows you to develop in a declarative style to reflect your problem domain. for example you can program in a syntax that is natural to general or special relativity, quantum mechanics, etc. * fast and free implementations such as SBCL * the language is standardized so your code will not need to be unnecessarily maintained (this is good

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for university professors that have repeating long-running courses) i am not sure what type of references you are asking for but i will list few open source projects that might be interesting to you: * CLASP - common lisp implementation build on top of LLVM. this project is headed by a computational chemist (prof. schafmeister) [0] * PetaLisp - for HPC [1] * MAGICL - Matrix Algebra proGrams In Common Lisp by Rigetti Computing [2] * Quil - quantum instruction language developed on top of common lisp [3] * CL-CUDA - use NVIDIA CUDA in Common Lisp programs [4] * MGL - Common Lisp machine learning library [5] * Maxima - a computer algebra system developed in common lisp [6] i think these are plenty to show that there exists serious interest in using common lisp for scientific computing. it is possible that some of the people behind these projects are on HN and maybe they can further expand on this [0] https://github.com/clasp-developers/clasp [1] https://github.com/marcoheisig/Petalisp [2] https://github.com/quil-lang/magicl [3] https://github.com/quil-lang/quilc [4] https://github.com/takagi/cl-cuda [5] https://github.com/melisgl/mgl also https://github.com/melisgl/higgsml [6] https://maxima.sourceforge.io/

In Idiomatic Clojure without sacrificing performance

patrec·Dec 30, 2020

Not really. But you might want to check out what Gábor Melis is doing, e.g. https://github.com/melisgl/mgl He's won several ML competitions and works at Deep Mind now, so as far as Common Lisp fans with strong street cred in up-to-date machine learning go, he's probably a good starting point.

In SBCL: New in Version 2.1.0

GreyZephyr·Mar 29, 2017

Wondering if anyone has any experience using lisp for machine learning? I'm aware of mgl[0], but it seems to be abandoned. The lack any wrappers for tensor flow or caffe is also a bit surprising to me. The cliki page [1] is also unhelpful and out of date. Is machine learning on lisp dead or are there projects out there that I'm just not aware of? [0] https://github.com/melisgl/mgl [1] http://www.cliki.net/machine%20learning

In How to write Common Lisp in 2017 – an initiation manual