Author

P. Sokerin

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Preprint Aug 2026

GENADA: efficient generative time series adversarial attack framework

A GENerative ADversarial Attack (GENADA) is proposed that learns a generative model to produce deceptive perturbations directly in a single forward pass and a procedure to train it, and achieves comparable attack quality to strong baselines while requiring less time to generate perturbations during inference.

M.A. Baronov, D. Vorobev, M. Rusanova et al. · 0 citations

#machine learning Preprint Aug 2026

CLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting

This work proposes CLaST, a VAE framework for probabilistic multivariate time series forecasting that learns embeddings that preserve contextual similarity between observations through the authors' contrastive loss function.

A. Marusov, D. Anikin, P. Sokerin et al. · 0 citations