ML Collective (MLC) is an independent, nonprofit organization with a mission to make research opportunities accessible and free by supporting open collaboration in machine learning (ML) research.

We execute our mission via three branches: (1) MLC: Lab conducts fundamental machine learning research via peer mentoring—researchers simply come on their own accord to help each other publish; (2) MLC: Reading promotes idea sharing via focused presentations and free-flowing discussions—one paper at a time; and (3) MLC: Open Collab is a 100% open community and culture where we provide company and assistance in each other's growth into an ML researcher with information and idea exchanges, social events, project-pitching meetings, and white-board-like discussions.

MLC can be a "research home" for you regardless of your affiliation, employer, or background.

News

  1. [Apr 2021] Our ICLR Social is accepted! See you at ICLR in the first week of May!
  2. [Apr 2021] Congratulations to Daniel D'souza and the Masakhane team on publishing MasakhaNER: Named Entity Recognition for African Languages! It is also Daniel's very first publication!
  3. [Apr 2021] The first research jam was a success! Our next research jam is on June 2!
  4. [Mar 2021] Working on an ML research project but don’t have labmates to show? Show a plot or three, or simply join to hang out in our Research Jam on March 31!
  5. [Mar 2021] We are having an OpenClubHouse event this Sunday, March 14 at 10am PT on our community Discord. No need of an iPhone or an invite.

View all news.

Get in Touch

  1. Follow our Twitter for major updates.
  2. Subscribe to our weekly reading group "Deep Learning: Classics and Trends."
  3. Join our Open Collab Discord server.
  4. Check out our events calendar (view, add to Google Calendar, iCal) to join an upcoming event.
  5. Watch our YouTube channel for research contents like RFPs.
  6. Send us an email at hello at mlcollective dot org.

Selected Projects

See all projects on MLC: Lab page.

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Plug and Play Language Models: a Simple Approach to Controlled Text Generation

Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, Rosanne Liu

Published at ICLR 2020

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An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, Jason Yosinski

Published at NeurIPS 2018

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Measuring the intrinsic dimension of objective landscapes

Chunyuan Li, Heerad Farkhoor, Rosanne Liu, Jason Yosinski

Published at ICLR 2018