Список тем летних стажировок! https://m.vk.com/@itmo.mllab-letnie-stazhirovki-2023

Channel
Machine Learning Lab | ITMO
@itmo_mllab
On this record: Growth · Engagement · Posts · Citations · Cite this entry
401subscribers
-1 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
Register entry
| Telegram ID | -1001459774752 |
|---|---|
| Type | Channel |
| Username | @itmo_mllab |
| Created | Between 1 April 2019 and 31 August 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 8 August 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 1 time, most recently 8 August 2026 |
| On Telegram | t.me/itmo_mllab |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 8 Aug 2026, 18:25 | 401 | -1 |
| 8 Aug 2026, 05:48 | 402 | no change |
| 7 Aug 2026, 21:29 | 402 | first reading |
Engagement
16 posts held, back to 20 October 2022 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 16 posts for this entry, the most recent from 9 July 2023. An engagement rate over an empty window would be a number about nothing.
Recent posts
15 мая защитил кандидатскую Вячеслав Шаламов 22 июня защитила кандидатскую Валерия Ефимова (защита была живой) Поздравляем ребят и желаем им дальнейших научных успехов!
Сегодня в 15:00 будет защита кандидатской диссертации Славы Шаламова "Дискретная оптимизация на основе управления ансамблем алгоритмов" Ссылка на YouTube трансляцию https://www.youtube.com/watch?v=CXLgMpeLer0&ab_channel=%D0%94%D0%A1%D0%98%D0%A2%D0%9C%D0%9E02.22.00
28 Apr, 16:00 online Maxim Kashirin, PhD student New Bayesian Focal Loss Targeting Aleatoric Uncertainty Estimate: Pollen Image Recognition Biological image recognition is one of the most challenging domains due to the similarity of species and errors in labeling. Pollen grain image classification heavily suffers from both problems preventing from building of well-calibrated recognition models. In our research, to …
https://youtu.be/FJtFh8SPXRQ
10 марта, 16:00 Иван Жарский, магистрант Векторная графика и её возможности: Применение методов машинного обучения для генерации и обработки векторных изображений. В настоящее время существует множество моделей для генерации растровой графики, однако чаще всего эти методы не позволяют синтезировать картинки сверхвысокого разрешения. Здесь на помощь приходит векторная графика, позволяющая масштабировать изображения …
Еще один анонс — 12 марта дедлайн на подачу статьи на конференцию AINL, которая будет проходить 20-22 апреля в гибридном режиме в Ереване и онлайн.
Dear all, we finally made it! We release the call for papers! Important deadlines are: 12th of March for paper submission and 19th of March for industrial talks. More details are available on the conference site and in the attached document.
Подошло время подавать тезисы докладов на КМУ, если вы этого ещё не сделали. Дедлайн на подачу – 20 февраля, а сама КМУ будет проходить 3-6 апреля. КМУ – это локальная конференция, площадка для обсуждения текущих исследований и результатов. В этом году лаборатория организует четыре подсекции (в рамках секции "Технологии программирования, искусственный интеллект, биоинформатика"): 1. Генеративный искусственный интел…
ChatGPT умеет многое, но как у него дела с юмором? Мы исследовали этот вопрос: https://habr.com/ru/post/715480/
10 Feb, 16:00, online Nikita Detkov, PhD student Fine-tuning of large text-to-image models: where all these AI-generated profile pictures came from Last year, it was a boom in text-to-image models. People have seen beautifully generated images of an astronaut riding a horse, argued on the art ethics, and tried to understand how it can be used in the real world. But even if those models do understand the corresponde…
6 Jan, 16:00, online Ali Mohammad, PhD student Different perspective on text/image matching Cross-modality information retrieval is a popular research task that grew more important as cross-modality data becoming more common on the internet, In my seminar talk, I will discuss the usage of transformer architecture for textual and visual modalities and training tasks that can be used to improve its performance, inclu…
Showing the 12 most recent of 16 posts we hold for @itmo_mllab. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Forward network
Republishes
Channels on the register whose posts this channel has forwarded.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 8 August 2026 — this entry's latest reading, not the date you are reading this.
“Machine Learning Lab | ITMO” (@itmo_mllab), 401 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/itmo_mllab.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.