Интеллектуальный анализ данных (О.Ю. Бахтеев, В.В. Стрижов)/Осень 2022

Материал из MachineLearning.

(Различия между версиями)
Перейти к: навигация, поиск
(Schedule and grading)
Текущая версия (05:28, 14 сентября 2022) (править) (отменить)
(Course page, and projects)
 
(14 промежуточных версий не показаны.)
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=Intelligent data analysis=
=Intelligent data analysis=
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This course develops skills of communications. The goal is to deliver your message to wide auditory of professionals. The form of delivery is a short paper. It results several discussions in our team according to the plan below.
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This course develops skills of communication. The goal is to deliver your message to wide auditory of professionals. The form of delivery is a short paper. It results several discussions in our team according to the plan below.
==Schedule and grading==
==Schedule and grading==
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*Dec: 2 link, 9 fin
*Dec: 2 link, 9 fin
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Insert your name and direct link to materials
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Insert your name and direct link to materials. Each column must carry your name.
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==Course page, and projects==
==Course page, and projects==
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* TODO Course page
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* TODO [https://intsystems.github.io/ru/course/ Course page]
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* TODO Projects
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* Course repository [https://github.com/intsystems/IDA GitHub]
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The result links
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The result links '''before 2nd of december'''
* Bronstein, M. [https://medium.com/towards-data-science/temporal-graph-networks-ab8f327f2efe Temporal Graph Networks], Medium TDS
* Bronstein, M. [https://medium.com/towards-data-science/temporal-graph-networks-ab8f327f2efe Temporal Graph Networks], Medium TDS
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*
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* Benj, E. [https://arstechnica.com/information-technology/2022/09/with-stable-diffusion-you-may-never-believe-what-you-see-online-again/ With Stable Diffusion, you may never believe what you see online again
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AI image synthesis goes open source, with big implications], Arstechnica
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* MIPT/Strijov, V. [https://www.eurekalert.org/news-releases/871622 Chip controlling exoskeleton keeps patients' brains cool], AAAS ([https://phys.org/news/2018-09-linear-equations-impaired-motion.html variant] Phys.org)
==Topics to discuss==
==Topics to discuss==
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* Differential alignment of continuous-time (series) videos [2104.13478]
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* Taken's theorem and convergent cross-mapping (signals) [or 2208.10981]
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* Graph diffusion models with PDE examples (flows, signals,videos) [2106.10934]
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* or probabilistic diffusion models [2208.11970]
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* Dimensionality reduction on Riemannian manifolds (for videos) [1605.06182]
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* Applications of Lagrangian, Hamiltonian and Noetherian neural PDEs [colab Severilov] [or 2208.06120]
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*
==Examples and references==
==Examples and references==
* [https://towardsdatascience.com/questions-96667b06af5#dee8 TDS guidelines]
* [https://towardsdatascience.com/questions-96667b06af5#dee8 TDS guidelines]
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* [https://nplus1.dev/blog/2022/04/01/samotek N+1 samotek]
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* [https://www.datasciencecentral.com/write-for-us/ DSC write]

Текущая версия

Each Saturday 13:10 at the channel m1p.org/go_zoom


Intelligent data analysis

This course develops skills of communication. The goal is to deliver your message to wide auditory of professionals. The form of delivery is a short paper. It results several discussions in our team according to the plan below.

Schedule and grading

Workflow

  1. Select topic (report)
  2. Prepare material (present 5-10 min and discuss)
  3. Make presentation (20 min and questions)
  4. Write your text (2 pages and discuss)
  5. Publish your text (link)

Calendar

  • Sep: 16, 23, 30 select
  • Oct: 7, 14, 21, 28 talk
  • Nov: 4, 11 talk, 18, 25 text
  • Dec: 2 link, 9 fin

Insert your name and direct link to materials. Each column must carry your name.

Date Select Talk Text
16nxt Islamov, Strijov
23sep ...
30 ...
7oct x ...
14 ...
21 ...
28 ...
4nov ...
11 ...
18 x ...
25 x ...

Course page, and projects

The result links before 2nd of december

AI image synthesis goes open source, with big implications], Arstechnica

Topics to discuss

  • Differential alignment of continuous-time (series) videos [2104.13478]
  • Taken's theorem and convergent cross-mapping (signals) [or 2208.10981]
  • Graph diffusion models with PDE examples (flows, signals,videos) [2106.10934]
  • or probabilistic diffusion models [2208.11970]
  • Dimensionality reduction on Riemannian manifolds (for videos) [1605.06182]
  • Applications of Lagrangian, Hamiltonian and Noetherian neural PDEs [colab Severilov] [or 2208.06120]

Examples and references

Личные инструменты