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- The course is a [i]regular [re]search seminar, mentored in a "family" manner. In this term, the seminar [sub]topic is Tame the complexity. It is mandatory that the enrolled student give a presentation on an agreed-upon topic (of interest, or her thesis, or he will talk about a research paper or area) once during the term. Topics of presentations focus primarily (but not necessarily) on those related to machine learning, representation learning, and scientific visualization or related to our subtopic (tackling complexity by LLM agents, proving P=NP, etc. ;-).
- There is a discussion group with official course information and a communication channel in addition to the
course outline below: watch both frequently!
Topics and Course Outline
Week 1– 19.9. canceled due to floods
- Readings: Motivating video: DEK's advice to young students.
Week 2 – Introduction, research strategy, evaluation, and course schedule planning
Join us at A502, Faculty of Informatics MU, on September 26th at 10 AM (CET) [or on Zoom, on-demand only].
- Why? Put readers in your place! Specifics of CS research and doctoral studies and their evaluation at FI MU: CS conference rankings
- How? to write
- What (and where)? It is important to "sell the ideas and work," pick the right topics and questions, research "big issues," and pick the right publication forums (in CS and NLP). An h-index as a measure of impact. The danger of Tyranny of metric.
Week 3 – Taming the complexity in/of your projects
Join us at A502, Faculty of Informatics MU, on Oct 3rd at 9:50 AM (catering preparation) and 10 AM (Invitation of newcomers, questions on readings, and a summary of Week 2). For joining by Zoom, ask in advance for a password.
We will present the research projects we are working on, in an elevator-pitch style. Presenting complex projects under these time constraints puts pressure on the compact style of presentation where each word or diagram matters and is challenging.
Week 4 – 10. 10. canceled
Week 5 – 17. 10. Ondřej Sojka
Join us at A502, Faculty of Informatics MU, on Oct 3rd at 9:50 AM (catering preparation) and 10 AM (Invitation of newcomers, questions on readings, and speaker introduction). For joining by Zoom, ask in advance for a password.
Hyphenation patterns play a vital role in enhancing the readability and
aesthetics of text, especially for Slavic languages. Current hyphenation
systems for many Slavic languages are outdated, sometimes relying on
manually created patterns with limited effectiveness. We explore the
transfer learning of syllabic hyphenation patterns across multiple Slavic
languages to develop improved, data-driven hyphenation systems.
By using the International Phonetic Alphabet (IPA) as an intermediary, this research transfers hyphenation patterns between related Slavic languages, creating a unified set of IPA-based rules. These IPA patterns are then used to generate language-specific hyphenation patterns for each target language. The proposed approach aims to develop reliable hyphenation patterns using machine learning methods, improving syllabification across multiple languages.
Although the work is ongoing, early results indicate promising improvements, particularly for Ukrainians. The new patterns are intended to be practical and easy to reproduce, ultimately contributing to better text layout quality for Slavic languages.
Kapitola obsahuje:
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Obrázek
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Studijní text
Učitel doporučuje studovat od 12. 10. 2024 do 20. 10. 2024.
Hyphenation patterns play a vital role in enhancing the readability and aesthetics of text, especially for Slavic languages. Current hyphenation systems for many Slavic languages are outdated, sometimes relying on manually created patterns with limited effectiveness. We explore the transfer learning of syllabic hyphenation patterns across multiple Slavic languages to develop improved, data-driven hyphenation systems. By using the International Phonetic Alphabet (IPA) as an intermediary, this research transfers hyphenation patterns between related Slavic languages, creating a unified set of IPA-based rules. These IPA patterns are then used to generate language-specific hyphenation patterns for each target language. The proposed approach aims to develop reliable hyphenation patterns using machine learning methods, improving syllabification across multiple languages. Although the work is ongoing, early results indicate promising improvements, particularly for Ukrainians. The new patterns are intended to be practical and easy to reproduce, ultimately contributing to better text layout quality for Slavic languages.
Week 6 – 24. 10.
Join us at A502, Faculty of Informatics MU, on Oct 3rd at 9:50 AM (catering preparation) and 10 AM (Invitation of newcomers, questions on readings, and a summary of Week 2). For joining by Zoom, ask in advance for a password.
Week 7 – 31. 10.
Week 8 – 7. 11. Marek Kadlčík
Week 9 – 14. 11. Tereza Vrabcová
Week 10 – 21. 11. Martin Kňažovič
Week 11 – 28. 11. Michal Štefánik
Join us at A502, Faculty of Informatics MU, on November 28th at 10 AM (CET) [and on Zoom, on-demand only].
Week 12 – 5. 12. Frank Mittelbach
Join us at A502, Faculty of Informatics MU, on December 5th at 10 AM (CET) or [or on Zoom].
Week 13 – 12. 12. Merry Christmas
Join us at A502, Faculty of Informatics MU, on December 12th at 10 AM (CET) or [or on Zoom].
Tips for readings, discussions, and presentation preparations:
- Top2Vec towardsdatascience.com/top2vec-new-way-of-topic-modelling
- How to Speak by Patrick Winston (YouTube video)
Žákovi, který se hrozil chyb, Mistr řekl: "Ti, kdo nedělají chyby, chybují nejvíc ze všech – nepokoušejí se o nic nového." Anthony de Mello: O cestě
To a student in danger, the Master said: "Those who do not make mistakes most of all – they do not try anything new." Anthony de Mello