April 30 anywhere on Earth (= Friday May 1st, 2pm Warsaw time) passed
Notification of acceptance:
31 May 2020 anywhere on Earth 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 travel grants will be offered to participants, based on financial considerations only. The grants will cover fully or partially the costs of attending the school (travel tickets, accommodation, registration fee). The request for travel grant will be submitted along with the application to the school. If you need a visa for travelling to Poland (see FAQ ), we will provide the necessary invitation letter once the acceptance results are out.
To apply for attending the school, you will be required to 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 (min 150 words, max 500 words): describe your research interests, projects related to ML you worked on or would like to work on, motivation for attending the summer school and so forth.
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 travel grant request if applicable; see FAQ .
You will need to create an account in our application system to apply. We encourage potential participants to think about the project they want to submit even before the system is up.
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 have to 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.
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 system, you will need to indicate the option you chose.
Evaluation of the abstracts will be based on the aspects below:
Clarity of presentation (good writing, includes figures, etc)
Relevance for the school theme
Properly contextualising the work within the literature (discussing briefly related work)
Experiments if any, interpretation of the results, and conclusion
Clarity of the description of the paper reproduced
Understanding of the paper reproduced
Clear description of the reproduction setting / implementation
Discuss results of the reproduction
Comments on the difficulty of reproducing results / criticisms
(bonus) Further exploration of the method, e.g. robustness to hyper-parameters, additional testing on a different dataset, etc.
Clear description of the reviewed papers
Understanding of the reviewed papers
Comparison between the 3 papers, highlight advantages and downsides for each
(bonus) Provide additional interpretation / insights on the works, or re-interpret one work in the light of the other
Please contact us at email@example.com if you have any questions regarding your choice of abstract, or the application process in general.