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From: Nathan Dehnel <ncdehnel@gmail.com>
To: rprior@protonmail.com, guix-devel@gnu.org
Subject: Guidelines for pre-trained ML model weight binaries (Was re: Where should we put machine learning model parameters?)
Date: Fri, 7 Apr 2023 00:50:31 -0500	[thread overview]
Message-ID: <CAEEhgEuT+YnGZMFB=v=zM56RfOULbXdxt4mHBXp8_X+eJM6Htg@mail.gmail.com> (raw)

I am uncomfortable with including ML models without their training
data available. It is possible to hide backdoors in them.
https://www.quantamagazine.org/cryptographers-show-how-to-hide-invisible-backdoors-in-ai-20230302/


             reply	other threads:[~2023-04-07  5:51 UTC|newest]

Thread overview: 22+ messages / expand[flat|nested]  mbox.gz  Atom feed  top
2023-04-07  5:50 Nathan Dehnel [this message]
2023-04-07  9:42 ` Guidelines for pre-trained ML model weight binaries (Was re: Where should we put machine learning model parameters?) Simon Tournier
2023-04-08 10:21   ` Nathan Dehnel
2023-04-11  8:37     ` Simon Tournier
2023-04-11 12:41       ` Nathan Dehnel
2023-04-12  9:32         ` Csepp
2023-09-06 14:28 ` Guidelines for pre-trained ML model weight binaries Andreas Enge
  -- strict thread matches above, loose matches on Subject: below --
2023-04-03 18:07 Guidelines for pre-trained ML model weight binaries (Was re: Where should we put machine learning model parameters?) Ryan Prior
2023-04-03 20:48 ` Nicolas Graves via Development of GNU Guix and the GNU System distribution.
2023-04-03 21:18   ` Jack Hill
2023-04-06  8:42 ` Simon Tournier
2023-04-06 13:41   ` Kyle
2023-04-06 14:53     ` Simon Tournier
2023-05-13  4:13   ` 宋文武
2023-05-15 11:18     ` Simon Tournier
2023-05-26 15:37       ` Ludovic Courtès
2023-05-29  3:57         ` zamfofex
2023-05-30 13:15         ` Simon Tournier
2023-07-02 19:51           ` Ludovic Courtès
2023-07-03  9:39             ` Simon Tournier
2023-07-04 13:05               ` zamfofex
2023-07-04 20:03                 ` Vagrant Cascadian

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