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Marina Munkhoeva

6 accepted papers

2026

Modeling the Density of Pixel-level Self-supervised Embeddings for Unsupervised Pathology Segmentation in Medical CT

Mikhail Goncharov, Eugenia Soboleva, Daniil Ignatyev, Mariia Donskova, Mikhail Belyaev, Ivan Oseledets +2
ICLR 2026poster

Accurate detection of all pathological findings in 3D medical images remains a significant challenge, as supervised models are limited to detecting only the few pathology classes annotated in existing datasets. To address this, we frame pathology detection as an unsupervised visual anomaly segmentat…

Cited by 0Source ↗code ↗Scholar ↗
2025

Efficient Distribution Matching of Representations via Noise-Injected Deep InfoMax

Ivan Butakov, Alexander Semenenko, Alexander Tolmachev, Andrey Gladkov, Marina Munkhoeva, Alexey Frolov
ICLR 2025poster

Deep InfoMax (DIM) is a well-established method for self-supervised representation learning (SSRL) based on maximization of the mutual information between the input and the output of a deep neural network encoder. Despite the DIM and contrastive SSRL in general being well-explored, the task of learn…

Cited by 0Source ↗PDF ↗Scholar ↗
2023

Neural Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning

Marina Munkhoeva, Ivan Oseledets
NeurIPS 2023poster

Self-supervised methods received tremendous attention thanks to their seemingly heuristic approach to learning representations that respect the semantics of the data without any apparent supervision in the form of labels. A growing body of literature is already being published in an attempt to build…

Cited by 2Source ↗PDF ↗Scholar ↗
2022

CC-CERT: A Probabilistic Approach to Certify General Robustness of Neural Networks

Mikhail Pautov, Nurislam Tursynbek, Marina Munkhoeva, Nikita Muravev, Aleksandr Petiushko, Ivan Oseledets
AAAI 2022technical

In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks --- small modifications of the input that change the predictions. Besides rigorously studied $ell_p$-bounded additive perturbations, semantic perturbations (e.g. rotation, translation) raise…

Cited by 23Source ↗PDF ↗code ↗Scholar ↗
2020

The Shape of Data: Intrinsic Distance for Data Distributions

Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Alex Bronstein, Ivan Oseledets +1
ICLR 2020poster

The ability to represent and compare machine learning models is crucial in order to quantify subtle model changes, evaluate generative models, and gather insights on neural network architectures. Existing techniques for comparing data distributions focus on global data properties such as mean and co…

Cited by 63Source ↗Scholar ↗
2018

Quadrature-based features for kernel approximation

Marina Munkhoeva, Yermek Kapushev, Evgeny Burnaev, Ivan Oseledets
NeurIPS 2018spotlight

We consider the problem of improving kernel approximation via randomized feature maps. These maps arise as Monte Carlo approximation to integral representations of kernel functions and scale up kernel methods for larger datasets. Based on an efficient numerical integration technique, we propose a un…

Cited by 70Source ↗PDF ↗code ↗Scholar ↗