On Wed, Sep 11, 2019 at 8:41 PM Michael Wong <fraggamuffin@gmail.com> wrote:

SG19 Machine Learning 2 hours
Topic: ISOCPP SG19 Machine Learning
Time: May 9, 2019 1:00 PM Eastern Time (US and Canada)
    Every month on the Second Thu, until Sep 12, 2019, 5 occurrence(s)
    May 9, 2019 1:00 PM
    Jun 13, 2019 1:00 PM
    Jul 11, 2019 1:00 PM
    Aug 8, 2019 1:00 PM
    Sep 12, 2019 1:00 PM
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1. Opening and introductions

1.1 Roll call of participants

Michael Wong, Matthew Galati, Phil Ratzloff, Richard Dosselmann, Marco Foco, Frank Seide,  Ronan Keryell, Sebastian Messmer, Andrew Lumsdaine, William Tambellini

1.2 Adopt agenda


1.3 Approve minutes from previous meeting, and approve publishing
 previously approved minutes to ISOCPP.org


1.4 Action items from previous meetings

2. Main issues (125 min)

2.1 General logistics
All C++ reflector are now moved to listserv


Belfast  Mailing Oct 7. After our next call Oct 10.

2.2 Paper reviews

Review Belfast paper submission strategy

2.2.1: ML topics
Differentiable Programing by Marco Foco

differentiable programming (library solution, + other things)

Looking at:

Richard Dosselman
Math proposal for Machine Learning

Compute mean/median mode in one pass over set
Boost.accumulate also does one pass over dataset, connected with Eric Niebler

Vincent also suggest the following:

I would suggest an option 4 relying on the range library and inspired by
range adaptors (link
provide composable function objects for stats so that one could write :

auto my_stats = mean | median | mode; // Composes a function object
std::vector v = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
auto results = mystats(v); // Runs my_stats on vector v
std::cout << results.mean << " " << results.median << " " << results.mode
<< std::endl; // Display results

Ranges compiler needed. Talk to Chris Di Bella
Range V3 may not require concepts and will work with GCC and clang, and VS recent versions
does not have composite object



How does it compose with multi dimensional tensors, like a multi dim iterators? Yes we want it to compose
Can it be generalized to generic reductions? Don't know

Unlikely to be at Belfast.

Graph Proposal for Machine Learning


There is an updated Graph Proposal P1709R1 at


Rewritten for R1 as a functional interface, for accessing the graph
added const steps as a starting point as well as type traits
Need some workign examples
New BGL17 could help with this
There are descriptors and property maps. Property is still new in LEWG, so it is being vetted.


Phil may be. Barry Hedquist who is HoD for US INCITS/ANSI

AI: andrew and Phil will get together


Also Tree Proposal for Machine Learning by Vincent

D1416R1: SG19 - Linear Algebra for Data Science and Machine Learning

P1415: Machine Learning Layered list

2.2.2 SG14 Linear Algebra progress:
Different layers of proposal

2.2.3 any other proposal for reviews?

2.3 Other Papers and proposals

2.5 Future F2F meetings:

Andrew Sebastian Michael at CPPCON next week.

Machine Learning at CPPCON 2020.

2.6 future C++ Standard meetings:

   - *2019-11-04 to 09: Belfast, Northern Ireland;* Archer Yates

-2020-02-10 to 15: Prague, Czech Republic

- 2020-06-01 to 06: Bulgaria
- 2020-11: (New York, tentative)
- 2021-02-22 to 27: Kona, HI, USA

3. Any other business


New reflector


Old Reflector

Code and proposal Staging area

4. Review

4.1 Review and approve resolutions and issues [e.g., changes to SG's
working draft]

4.2 Review action items (5 min)

5. Closing process

5.1 Establish next agenda


5.2 Future meeting

Aug 8
Sep 12
Oct 10: Mailing deadline Oct 7, SC42 , tentative. Cancelled.
Nov 14 - cancelled due to DST change and switching to a new cycle.