Интеллектуальный анализ данных (О.Ю. Бахтеев, В.В. Стрижов)/Осень 2022
Материал из MachineLearning.
(Различия между версиями)
(→Course page, and projects) |
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(22 промежуточные версии не показаны) | |||
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=Intelligent data analysis= | =Intelligent data analysis= | ||
- | This course | + | 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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- | # Select topic (report) | + | Workflow |
+ | # Select topic (report) | ||
# Prepare material (present 5-10 min and discuss) | # Prepare material (present 5-10 min and discuss) | ||
# Make presentation (20 min and questions) | # Make presentation (20 min and questions) | ||
# Write your text (2 pages and discuss) | # Write your text (2 pages and discuss) | ||
# Publish your text (link) | # 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. | ||
+ | |||
+ | {|class="wikitable" | ||
+ | |- | ||
+ | ! Date | ||
+ | ! Select | ||
+ | ! Talk | ||
+ | ! Text | ||
+ | |- | ||
+ | |16nxt | ||
+ | | | ||
+ | |Islamov, Strijov | ||
+ | | | ||
+ | |- | ||
+ | |23sep | ||
+ | |... | ||
+ | | | ||
+ | | | ||
+ | |- | ||
+ | |30 | ||
+ | |... | ||
+ | | | ||
+ | | | ||
+ | |- | ||
+ | |7oct | ||
+ | |x | ||
+ | |... | ||
+ | | | ||
+ | |- | ||
+ | |14 | ||
+ | | | ||
+ | |... | ||
+ | | | ||
+ | |- | ||
+ | |21 | ||
+ | | | ||
+ | |... | ||
+ | | | ||
+ | |- | ||
+ | |28 | ||
+ | | | ||
+ | |... | ||
+ | | | ||
+ | |- | ||
+ | |4nov | ||
+ | | | ||
+ | |... | ||
+ | | | ||
+ | |- | ||
+ | |11 | ||
+ | | | ||
+ | |... | ||
+ | | | ||
+ | |- | ||
+ | |18 | ||
+ | | | ||
+ | |x | ||
+ | |... | ||
+ | |- | ||
+ | |25 | ||
+ | | | ||
+ | |x | ||
+ | |... | ||
+ | |- | ||
+ | |} | ||
==Course page, and projects== | ==Course page, and projects== | ||
- | * TODO Course page | + | * TODO [https://intsystems.github.io/ru/course/ Course page] |
- | * | + | * Course repository [https://github.com/intsystems/IDA GitHub] |
- | The result links | + | The result links '''before 2nd of december''' |
- | * Bronstein, M. [ | + | * Bronstein, M. [https://medium.com/towards-data-science/temporal-graph-networks-ab8f327f2efe Temporal Graph Networks], Medium TDS |
- | * | + | * 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 |
+ | AI image synthesis goes open source, with big implications], Arstechnica | ||
+ | * 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== | ||
- | + | * 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== | ==Examples and references== | ||
* [https://towardsdatascience.com/questions-96667b06af5#dee8 TDS guidelines] | * [https://towardsdatascience.com/questions-96667b06af5#dee8 TDS guidelines] | ||
+ | * [https://nplus1.dev/blog/2022/04/01/samotek N+1 samotek] | ||
+ | * [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
- Select topic (report)
- Prepare material (present 5-10 min and discuss)
- Make presentation (20 min and questions)
- Write your text (2 pages and discuss)
- 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
- TODO Course page
- Course repository GitHub
The result links before 2nd of december
- Bronstein, M. Temporal Graph Networks, Medium TDS
- 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
AI image synthesis goes open source, with big implications], Arstechnica
- MIPT/Strijov, V. Chip controlling exoskeleton keeps patients' brains cool, AAAS (variant Phys.org)
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]