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Daniil Cherniavskii

5 accepted papers

2026

Probing Newtonian Mechanics in Video Generative Models with Real Physical Systems

Antonios Tragoudaras, Daniil Cherniavskii, Chenyu Zhang, Antonis Vozikis, Thijmen Nijdam, Derck Prinzhorn +4
ICML 2026poster

Recent advances in image and video generation raise hopes that these models possess world modeling capabilities—the ability to generate realistic, physically plausible videos. This could revolutionize applications in robotics, autonomous driving, and scientific simulation. However, before treating t…

Cited by 0Source ↗Scholar ↗
2023

Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts

Eduard Tulchinskii, Kristian Kuznetsov, Kushnareva Laida, Daniil Cherniavskii, Sergey Nikolenko, Evgeny Burnaev +2
NeurIPS 2023poster

Rapidly increasing quality of AI-generated content makes it difficult to distinguish between human and AI-generated texts, which may lead to undesirable consequences for society. Therefore, it becomes increasingly important to study the properties of human texts that are invariant over text domains…

Cited by 82Source ↗PDF ↗code ↗Scholar ↗
2023

Learning topology-preserving data representations

Ilya Trofimov, Daniil Cherniavskii, Eduard Tulchinskii, Nikita Balabin, Evgeny Burnaev, Serguei Barannikov
ICLR 2023poster

We propose a method for learning topology-preserving data representations (dimensionality reduction). The method aims to provide topological similarity between the data manifold and its latent representation via enforcing the similarity in topological features (clusters, loops, 2D voids, etc.) and…

Cited by 26Source ↗PDF ↗code ↗Scholar ↗
2022

Acceptability Judgements via Examining the Topology of Attention Maps

Daniil Cherniavskii, Eduard Tulchinskii, Vladislav Mikhailov, Irina Proskurina, Laida Kushnareva, Ekaterina Artemova +4
EMNLP 2022finding

The role of the attention mechanism in encoding linguistic knowledge has received special interest in NLP. However, the ability of the attention heads to judge the grammatical acceptability of a sentence has been underexplored. This paper approaches the paradigm of acceptability judgments with topol…

Cited by 24Source ↗PDF ↗code ↗Scholar ↗
2021

Artificial Text Detection via Examining the Topology of Attention Maps

Laida Kushnareva, Daniil Cherniavskii, Vladislav Mikhailov, Ekaterina Artemova, Serguei Barannikov, Alexander Bernstein +3
EMNLP 2021main

The impressive capabilities of recent generative models to create texts that are challenging to distinguish from the human-written ones can be misused for generating fake news, product reviews, and even abusive content. Despite the prominent performance of existing methods for artificial text detect…

Cited by 56Source ↗PDF ↗code ↗Scholar ↗