Difference between revisions of "Step 1"
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# A clear message in the area of Theoretical Foundations of Machine Learning. | # A clear message in the area of Theoretical Foundations of Machine Learning. | ||
| − | # Top peer | + | # Top peer-reviewed journals, no ArXiv, better avoid conferences. |
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# No overviews, it is another genre. | # No overviews, it is another genre. | ||
# No Kaggle-style papers with messages like "It works, but nobody knows how". | # No Kaggle-style papers with messages like "It works, but nobody knows how". | ||
| − | # No papers from | + | # No papers from other fields: linguistics, medicine, finance, physics, etc. There must be only one primary subject: Machine Learning. |
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| + | === Recommended journals === | ||
| + | # [https://link.springer.com/journal/10994 Machine Learning] | ||
| + | # [https://www.sciencedirect.com/journal/expert-systems-with-applications Expert Systems with Applications] | ||
| + | # [https://www.jmlr.org/ Journal of Machine Learning Research] | ||
| + | # [https://www.sciencedirect.com/journal/artificial-intelligence Artificial Intelligence] | ||
| + | # [https://www.sciencedirect.com/journal/neurocomputing/vol/609/suppl/C Neurocomputing] | ||
| + | # [todo] | ||
| + | # [https://www.sciencedirect.com/journal/neural-networks/ Neural Networks] | ||
| + | # [https://www.sciencedirect.com/journal/pattern-recognition/vol/158/suppl/C Pattern Recognition] | ||
| + | # [https://link.springer.com/journal/10618 Data Mining and Knowledge Discovery] | ||
| + | # [https://www.nature.com/natmachintell/research-articles Nature Machine Inlelligence], the problem is the first word here is Nature so it focuses on the natural sciences | ||
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| + | See also | ||
| + | # [https://www.elsevier.com/open-access/open-archive Elsevier's open archive] | ||
| + | # [https://www.springeropen.com/collections?subject=Computer+Science Springer's open archive] | ||
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==Fun== | ==Fun== | ||
Revision as of 17:14, 19 September 2024
The most important things come first. We discuss the main message, delivered by a scientific paper. We explore the first three elements of a scientific paper: the abstract, the highlights, and the keywords. The main message shall reveal itself through all elements of the paper. But we leave the rest of it for the next time. Namely, the title, introduction, problem statement, goal of the computational experiment, and conclusion are left behind. We select a part and exercise in the reconstruction of these three elements.
Contents
The seminar
- The warm-up 3-minute test
- Model, Algorithm, Method: Machine learning in a nut-shell
- Step 0 homework results discussion
- Step 1 homework, how to read (how to search is a separate topic)
- Structure of the main message
- Structure of the abstract
- The second and the last slide of your talk
- Extracting keywords
- Highlights: compressing the paper
- Instastructure for your homework
- GitHub: organize the repository
- LaTeX: compile your file and commit without temporary files
- The papers to select from
- Optional GPT-role discussion
Resources
Step 1 Youtube video (expected with online version)
Homework
- Set up your GitHub repository using the template
- Select a paper to read from the list below
- Write your own
- Abstract
- Keywords
- Highlights
- Short motivation for why you selected this paper (no templates here it is an extra topic to discuss)
- Compile and upload TEX and PDF to GitHub (no temporary files, please)
- Linear models. Read pages XX from the book XX, for the next warm-up test
Papers to read
Can I select any paper from the internet by my own choice? – Yes. Here are the formal requirements.
- A clear message in the area of Theoretical Foundations of Machine Learning.
- Top peer-reviewed journals, no ArXiv, better avoid conferences.
- No overviews, it is another genre.
- No Kaggle-style papers with messages like "It works, but nobody knows how".
- No papers from other fields: linguistics, medicine, finance, physics, etc. There must be only one primary subject: Machine Learning.
Recommended journals
- Machine Learning
- Expert Systems with Applications
- Journal of Machine Learning Research
- Artificial Intelligence
- Neurocomputing
- [todo]
- Neural Networks
- Pattern Recognition
- Data Mining and Knowledge Discovery
- Nature Machine Inlelligence, the problem is the first word here is Nature so it focuses on the natural sciences
See also
Transcript of the video
Appears after the seminar.