Deadline for applications: March 29, 2019, 23:59 Eastern European Time passed Notification of acceptance: first round April 19, 2019 passed Notification of acceptance: second round April 26, 2019 passed
The school welcomes applications from candidates all over the world, regardless of their level of expertise in Machine Learning or whether they are students or not; see FAQ.
A limited number of scholarships will be offered to participants, based on financial considerations only. The request for scholarship should be submitted along with the application to the school.
If you need a visa for travelling to Romania (see FAQ), we will provide the necessary invitation letter once the acceptance results are out.
To apply for attending the school, you are required to fill the Application form below and provide the following information:
personal information (name, contact, affiliation etc.)
an up-to-date CV (your choice of template, must be a pdf file)
a statement of research interests: describe your research interests, projects related to ML you worked on or would like to work on, motivation for attending the summer school etc.
an extended abstract on (choose ONLY ONE):
(1) [Research] your own project related to Machine Learning, OR
(2) [Reproduction] a project reproducing the results of a paper published by someone else in a top conference or journal (e.g. NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, AAAI, ACL, EMNLP, TPAMI, JMLR) in the past 3 years, OR
(3) [Review] a review of 3 related research papers on an ML topic of your choice (e.g. 3 papers on continual learning, or 3 papers on attention models, etc), published by other authors in top conferences or journals (e.g. NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, AAAI, ACL).
information justifying the scholarship request if applicable; see FAQ.
You will need a gmail account to apply (for uploading files in the Application form). If you don't have one already or do not wish to use your current account, you can find instructions on creating a gmail account for free here.
Application form: The application period is now closed.
Notes regarding the extended abstract
We provide three options for the extended abstract to make sure that the selection process is accessible to anyone, irrespective of the level of expertise in Machine Learning. We will assess interest and potential in the field, not just expertise in ML.
Accepted candidates who choose options (1) or (2) will present the projects as posters at the school, during the poster sessions.
The extended abstract can be structured as a short paper. E.g., it can have sections (e.g. Intro, Method, Experiments, References etc.), it can include images / figures / tables etc.
The extended abstract must not exceed two pages, excluding references.
Submissions are not blind: author(s) names and affiliations should appear on the first page.
It is possible to submit the extended abstract of a research paper that you've already published (from 2015 onward); this falls under option (1). Summarise your paper to respect the page limit, keep the original authors list, and include a brief description of your contribution to the paper if you are not the main author or shared main author; include this as a footnote attached to your name.
Use whatever layout and editor you prefer; e.g. for latex you could use NeurIPS format and sharelatex.
We accept multiple applicants (normally 2) with the same extended abstract for options (1) and (2), provided that the applicants all contributed equally or considerably to the work. In your extended abstract, put the names of all the applicants and include a brief description of your contribution to the project, as a footnote attached to your name. Note that the selection decision takes into account several factors, not only the abstract, hence it can differ for candidates with the same abstract.
All documents must be submitted in pdf format.
Best poster awards will be selected during the school.
Recommendations for choosing among the three options of extended abstract:
If you are a PhD, postdoc, Master student, faculty, practitioner in ML etc., you should choose option (1) or (2); in particular consider option (1) if you are actively working (or worked recently) on an ML project to which you contributed considerably.
If you are not working actively in ML research, but have some familiarity with ML libraries (e.g. pytorch, Tensorflow etc.), you should choose option (2) or option (1).
If you are not working actively in ML research and are not familiar with ML libraries (i.e. you are only beginning in ML), you may choose option (3).
For option (1), it is fine if the work is in incipient stages and you only have preliminary results. This is a non-archival submission; you can submit your work to any other venue as well.
For option (2), please also include, if possible, your own observations about the paper, e.g. analysis of robustness, was the paper difficult to reproduce given the details in the paper; most important tricks in implementation etc.
For option (3), consider reviewing works that have some aspects in common, so that you can compare them and highlight their pros and cons.
In the application form, you will need to indicate the option you chose.
Please contact us at email@example.com if you have any questions regarding your choice of abstract, or the application process in general.