Application

Important dates

Applications open! Fill in the form below!

Applications close: April 15th 2021, 23:59 Anywhere on Earth passed

Notifications of acceptance: May 7th, 2021

Application instructions

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.

To apply for attending the school, please fill in the form below. 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 500 characters, max 2000 characters): 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).

Note: If you attended EEML2020 and won a spot for this year in the project or poster competition, please fill in the same application form and instead of submitting an abstract, please upload a pdf with the mention "I won the project/poster competition in EEML2020" and include the team and project that you were part of in EEML2020, or the title of your poster.

To fill in the application form, follow this link APPLICATION FORM

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.

  • 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 system, you will need to indicate the option you chose.


Evaluation of the abstracts will be based on the aspects below:

Research abstract:

  • 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

  • (bonus) Novelty

Reproduction:

  • 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.

Review:

  • 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 contact@eeml.eu if you have any questions regarding your choice of abstract, or the application process in general.