Latest news and updates from DSML Kazakhstan community
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DSML Meetup 2025 will take place at MOST IT Hub Almaty on July 5. Join us for new connections and engaging talks from dsml.kz residents:
We will discuss how problems were prepared for Kazakhstan's first national AI olympiad; how generative models are used in medical neuroimaging; how machine-learning approaches differ between academic research and industry; world models in modern reinforcement learning; and much more.
July 5. Doors open at 11:30; talks begin at 12:00. MOST IT Hub, Khodzhanova 2/2. Admission: 5,000 tenge, with a 50% discount for students and school students. Registration: forms.gle/az9sGAzZacJw5Zb56
Questions: @DSMLmeetup
Six years have passed since our last large in-person meetup in 2019. On July 5, our community will finally gather again at Most Hub in Almaty to discuss the latest AI news, hear engaging talks from residents, and simply spend time together!
We are opening speaker applications. If you have an interesting topic, research project, or product you would like to present, message @ayana_mussabayeva.
DSML Reading Club is a series of informal online community meetings where members share interesting ideas, recent papers, and their own research. If you recently read a great paper, published your work in a journal or at a conference, or simply want to discuss an interesting topic, join us as a speaker!
Complete the registration form. We will contact you and help organize the meeting.
Beetech 2025 took place last Saturday. Alongside the General track, this year's conference featured an AI & Beyond track, where members of our community delivered half of the talks.
Abylaikhan Turlasov shared guided-decoding techniques for working with LLMs. Mikhail Shkorin explained how to prevent video substitution during authentication. Dias Khalniyazov discussed the details of building an AI parser, while Renat Alimbekov broke down common mistakes in AI/ML projects and answered an important question: Is Lockheed real?
The day after tomorrow, Assel Yermekova will present a paper she co-authored, “Improved Sampling Algorithms for Lévy-Itô Diffusion Models”.
Recent work showed that Lévy–Itô diffusion models with isotropic α-stable noise improve image generation on imbalanced data. However, existing sampling algorithms solve only approximate reverse equations, which reduces quality. In this paper, we propose a family of stochastic differential equations with identical marginal distributions and show that parameter selection improves quality with few reverse-diffusion steps. We also demonstrate Lévy–Itô models across different domains and the advantages of text-to-speech models on highly imbalanced data.
At the meeting, we will discuss:
