Our Story

ML Collective was born from Deep Collective, a research group founded by Jason Yosinski and Rosanne Liu at Uber AI Labs in 2017. We started that group to foster open research collaboration and free sharing of ideas, and in 2020 we moved the group outside Uber and renamed it to MLC. Over the years we have aimed to build a culture of open, cross-institutional research collaboration among researchers of diverse and nontraditional backgrounds. Our weekly paper reading group, Deep Learning: Classics and Trends, has been running for three years and is open to the whole community.

Of course we love all of our papers, but a few favorites include Measuring Intrinsic Dimension, CoordConv, and Plug and Play Language Models.

Our Mission

Our mission is to facilitate open collaboration and free mentorship by providing a home for anyone with AI research passion, ideas, and expertise, who is interested in advancing both their professional careers and AI research at large. We produce open-access research (similarly to an academic or industry lab), including scientific publications, reports, blog posts, educational videos, open-source software, and demonstrations, and with these nurture the growth of researchers.

Common workplaces for researchers tend to standardize their employees’ growth path, so much so that there exists a numbered level system, or a set of permitted titles that researchers climb up through. Instead we think research should be more like art, and researchers should freely look for opportunities to polish their profile like actors going to auditions. Recent efforts in democratizing AI, including MOOCs, open-source software, and open-access scientific articles, among other means have greatly lowered the bar to individual knowledge acquisition. The growth into a skillful individual researcher is now approachable from various backgrounds. However, the bar to high quality collaborative research is still high and at times inaccessible to individuals of qualified abilities who come from underrepresented backgrounds or disciplines. ML Collective supports unaffiliated researchers, and at the same time encourages affiliated researchers to broaden and diversify their collaborations and influence.

We believe open collaboration is one key to further democratizing AI research.


Q: What does the name “ML Collective” mean?

A: “ML” is short for machine learning, our overall research direction. Our “Collective” is a group of genuinely curious people who simply enjoy working together.

Q: What do you do?

A: A few things! First, the lab aspect of ML Collective performs ML research and shows it to the world. This includes producing peer-reviewed papers, articles, blog posts, educational videos, technical demonstrations, software packages, and other products that showcase a unique finding or help further our understanding of machine learning. In other words, we're a research group, like those in academia or in corporations with a research division, but not!

The community building aspect of ML Collective include an open reading group and an open collaboration community to help promote accessibility in machine learning research.

Finally, we provide services to the community including draft proofreading before conference deadlines, and grad school application pre-reviewing year round; our members are also often open to chat about career paths, research retrospectives, or anything related to ML/AI research. If you have a draft or application you'd like us to take a look at, or if you'd like to chat about careers or research, drop us a line at hello at mlcollective dot org!

Q: What core research directions does ML Collective pursue?

A: Our past work has mostly focused on exposing and understanding neural network representations, optimization, and training dynamics, and using this understanding to design better models. That said, research directions are driven by individual ML Collective members, who may choose to take things in very different directions!

Q: Who is ML Collective for?

A: We provide a default home for ML researchers without affiliations, support aspiring researchers from underrepresented and non-traditional backgrounds, and we hope to attract established researchers who care deeply about diversity, fairness and equality of the community, eager to give back, and open to produce science in an open, collaborative environment. We hope researchers join with the belief that scientific advancements do not need to be profit-driven (and in fact, should inherently be nonprofit), success does not rely upon quantifiable achievements (but can be about how much one helps others, and enables the holistic progress of the community), mentorship does not only come with hierarchy or tenure (instead, we all learn from each other’s expertise), and open collaboration is foundational to both the growth of individuals, and the scientific advancement of the field.

Q: Is ML Collective an affiliation? If I’m a member of ML Collective, can I or do I have to use it on my publications?

A: Yes, members may use ML Collective as an affiliation on papers they publish. They do not have to use it on every paper they publish, but they may, as they prefer.

Q: Can I join if I already have a full-time job?

A: Yes! But you’ll need to make sure that any work you choose to share with the group can in fact be shared as per your employer’s policies.

Q: What can I get out of MLC?

A: If you are an unaffiliated researcher, MLC can simply be your home — where you find collaborators, project ideas, mentorship and advice, support and assistance. If you are already affiliated, MLC can be a place where you broaden and diversify your collaboration circle, craft your mentorship skill, and give back to the research community.

For example, MLC could be for you if:

  1. You are excited about research but have not yet developed a strong research direction and are looking to join a new project;
  2. You have research ideas you’d like to pursue and are looking for collaborators;
  3. You are working on your own research project in a vacuum and could simply use people to bounce your ideas and results off of;
  4. You simply want to learn more about machine learning and are interested in doing so with a community;
  5. You are interested in seeing how and if research within a non-hierarchical, non-monetized organization could work out, and are happy to be part of the experiment;
  6. You already have a research affiliation, collaborators, and a clear direction, but look to give back to society by offering advice, feedback, and mentorship to MLC members. You can bring others’ projects to the next level by sharing your expertise, and help researchers grow by influencing them with your unique experiences.

Q: Does ML Collective charge a membership fee or pay researchers?

A: No to both. MLC does not collect membership or event participation fees. We're writing grants and working on being able to accept donations to cover operating expenses.

Q: How do I join and contribute to ML Collective?

A: All three branches of MLC are actively looking for contributors! MLC: Reading and MLC: Open Collab are open to the public (see those pages for how to participate). Once joined, you can start contributing there by attending, helping organizing and promote our events, nominating topics, suggesting changes, all in all helping strengthen our community by being active, friendly, and generous in sharing knowledge and bringing new ideas to it. To join MLC: Lab could be a longer process, depending on how ready and committed you are about working on research with us. We require new members to join with at least one active research project, meaning she would be leading, and responsible for the success of, that project and committed to giving regular updates for it at our research meetings. It can be something you are already working on, or something we work together to find for you. Get in touch with hello at mlcollective dot org with your research background, interests, and other information so we can start brainstorming research ideas!