Potential participants are required to submit the following set of documents and complete a small test:
➔ CV
Describing your: education level, participation in scientific work, scientific events and teaching activities. Achievements: published articles, relevant work experience, topics of interest, thesis topics with the place of graduation. ML/DL tool skills and proficiency. 1-2 pages.
➔ Motivation letter;
Up to 1000 words long in which it is necessary to disclose: motivation to participate in school, ML/DL experience, if available, school expectations and future career plans
➔ Test with 10 questions on relevant calculus, statistics, machine learning and deep learning;
➔ Review Presentation: slides (pdf) + video recording (mp4/avi/mov/webm). Video recording is optional but it will give you extra points.
There are three possible options for a review:
- A description of your own ML/DL research
- A reproduction of the results of an article from one of the school's topics over the past 5 years. Adding ideas for improvement will be considered an additional plus to your application.
- A review of at least 3 articles on one of the school's topics
The video should last 5-10 minutes; ensure it captures your face, voice, and the slides. Upload the video to a cloud drive and don't forget to give us access to view it.
Review requirements
Articles for review and reproduction should be from A* rank conferences (NeurIPS, ICML, CVPR, etc).
The presentation should include:
- Introduction
- Problem statement
- Methods
- The analytical table
- Link to the repository if the article is being reproduced
Requirements for the analytical table:
- Analysis of trends on the chosen topic over the past 5 years based on articles, patents and open sources (NeurIPS, CVPR, etc.).
- Comparison of datasets (size, domain, preprocessing) and justification of their choice.
- Evaluation metrics with an explanation of why some metrics are used more often than others and what problems they may have.
- Analysis of SOTA models: analysis of efficiency on different tasks, complexity of implementation.
- Comparison of implementations in repositories (Hugging Face, GitHub, Papers with Code): pros and cons, configuration difficulties.
- You can use the first table here as an example.