I sure hope this catches on, but we should all be aware of the hurdles: - Little incentive for researchers to do this beyond their own good will. - Most ML researchers are bad writers, and it's unlikely that the editing team will do the work needed (which is often a larger reorganization of a paper and ideas) to improve clarity. - Producing great writing and clear, interactive figures, and managing an ongoing github…
You're absolutely right that this is a lot of work, and not many ML researchers have all the skills needed for it. In the short term, Distill's editorial assistance will help authors produce outstanding papers, although they need to be willing to work as well. In the longer-term, I'd like to explore match making between data visualization people who would like to get into machine learning and machine learning researc…
Distill: a modern machine learning journal
41–50 of 109 posts
Re: Distill: a modern machine learning journal
#42Re: Distill: a modern machine learning journal
#43I sure hope this catches on, but we should all be aware of the hurdles: - Little incentive for researchers to do this beyond their own good will. - Most ML researchers are bad writers, and it's unlikely that the editing team will do the work needed (which is often a larger reorganization of a paper and ideas) to improve clarity. - Producing great writing and clear, interactive figures, and managing an ongoing github…
You're right, i've been myself using git, github, keynote, ffmpeg, medium, JS, python, d3 and others to build blog post. I clearly don't expect people to do that much. I can only do that because i'm coming from web development, and very nice tools started to appear recently. People in research needs a design framework like a set of templates for keynotes/PPT/JS/CSS (think about how much traction got bootstrap). Disti…
Re: Distill: a modern machine learning journal
#44You should definitely assign a DOI to each article.
Re: Distill: a modern machine learning journal
#45Earlier quoted context omitted.
As I said in Rob's thingy, I hope you get the tenure committees and job committees, because they don't have to respect it but they're the ones you have to get to respect
All we can do is work hard to build academic support: * In the last three weeks, we've had 80 outreach conversations with various stakeholders for Distill. The majority of these have been academic researchers. The response has been extremely positive. * A number of ML faculty at Stanford / Berkeley / Toronto / Montreal are very excited and supportive of Distill. * Distill's steering committee consists of recognized l…
Yet I don't see how this will readily support possibly cutting-edge work or new research in machine learning that does not have access to visualization development, or these forged connections to Distill to facilitate the development of these visualizations.
So it seems like a likely outcome is that Distill publishes content from well-regarded institutions and increases publicity for that work, to the detriment of a vast bulk of papers which do not have access to the visualization resources to develop Distill-ed versions of their work.
Furthermore, and this is a larger disciplinary issue, but it seems inherently this could end up spotlighting more CS-y machine learning vs statistical learning due to cultural differences between disciplines and differences in computational/web development background in grad students and researchers in both fields. Are there efforts to reach out to statistical associations as well?
Re: Distill: a modern machine learning journal
#46I sure hope this catches on, but we should all be aware of the hurdles: - Little incentive for researchers to do this beyond their own good will. - Most ML researchers are bad writers, and it's unlikely that the editing team will do the work needed (which is often a larger reorganization of a paper and ideas) to improve clarity. - Producing great writing and clear, interactive figures, and managing an ongoing github…
I won't deny the time commitment needed for a distill article is not trivial - it is far more work than a technical blog. But in terms of a pure tradeoff of time per publication, the calculus makes sense. Most of the work of research distillation and synthesis is already part of the research process, and writing a distill article is just a matter of putting it all of down on paper. Doing research is a far more time consuming and less predictable process.
Re: Distill: a modern machine learning journal
#47I sure hope this catches on, but we should all be aware of the hurdles: - Little incentive for researchers to do this beyond their own good will. - Most ML researchers are bad writers, and it's unlikely that the editing team will do the work needed (which is often a larger reorganization of a paper and ideas) to improve clarity. - Producing great writing and clear, interactive figures, and managing an ongoing github…
Re: Distill: a modern machine learning journal
#48Various announcements: Google Research: https://research.googleblog.com/2017/03/distill-supporting-c... DeepMind: https://deepmind.com/blog/distill-communicating-science-mach... OpenAI: https://openai.com/blog/Distill/ YC Research: http://blog.ycombinator.com/distill-an-interactive-visual-jo... Chris Olah: http://colah.github.io/posts/2017-03-Distill/
Thank you for this effort. I'm a fan of your blog articles. A question regarding Distill: is it a journal like conventional journal to target new research? Or it is a journal for educational articles to explain old researches better?
I hope to contribute to an effort to better explain deep learning. I don't know if that is what distill is looking for?
Re: Distill: a modern machine learning journal
#49You should definitely assign a DOI to each article.
Each article currently gets a DOI DOI: http://doi.org/10.23915/distill ISSN: 2476-0757