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Multi-Constraint Safe RL with Objective Suppression for Safety-Critical Applications
A novel approach to tackle the multiple constraints challenge in Safe RL.
Zihan Zhou, Jonathan Booher, Wei Liu, Aleksandr Petiushko Александр Петюшко, Animesh Garg
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Multi-Constraint Safe RL with Objective Suppression for Safety-Critical Applications
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
Elegant way of measuring both aleatoric and epistemic uncertainties for neural nets.
Nikita Kotelevskii, Aleksandr Artemenkov, Kirill Fedyanin, Fedor Noskov, Alexander Fishkov, Artem Shelmanov, Artem Vazhentsev, Aleksandr Petiushko Александр Петюшко, Maxim Panov
PDF Cite Slides Source Document arXiv OpenReview Conf
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
Smoothed Embeddings for Certified Few-Shot Learning
Geometrical approach for certified few-shot learning via randomized smoothing of templates.
Mikhail Pautov, Olesya Kuznetsova, Nurislam Tursynbek, Aleksandr Petiushko Александр Петюшко, Ivan Oseledets
PDF Cite Poster Slides Source Document arXiv OpenReview Conf
Smoothed Embeddings for Certified Few-Shot Learning
Certified Robustness via Randomized Smoothing over Multiplicative Parameters of Input Transformations
A novel method of certified robustness using Rayleigh dostribution for smoothing when transformations consist not additive but multiplicative group.
Nikita Muravev, Aleksandr Petiushko Александр Петюшко
PDF Cite Video Source Document DOI arXiv
Certified Robustness via Randomized Smoothing over Multiplicative Parameters of Input Transformations
Quadric Hypersurface Intersection for Manifold Learning in Feature Space
A novel approach to use the so-called “Manifold Hypothesis” in order to solve the outlier detection problem by quadrics on top of FaceID embeddings.
Fedor Pavutnitskiy, Sergei O. Ivanov, Evgeniy Abramov, Viacheslav Borovitskiy, Artem Klochkov, Viktor Vyalov, Anatolii Zaikovskii, Aleksandr Petiushko Александр Петюшко
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Quadric Hypersurface Intersection for Manifold Learning in Feature Space
CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks
A theoretical approach to estimate the certified robustness tested on different image transformations.
Mikhail Pautov, Nurislam Tursynbek, Marina Munkhoeva, Nikita Muravev, Aleksandr Petiushko Александр Петюшко, Ivan Oseledets
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CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks
Geometry-Inspired Top-K Adversarial Perturbations
A simple geometrical approach for crafting top-k adversarial perturbations.
Nurislam Tursynbek, Aleksandr Petiushko Александр Петюшко, Ivan Oseledets
PDF Cite Video Source Document DOI arXiv
Geometry-Inspired Top-K Adversarial Perturbations
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
Elegant way of measuring both aleatoric and epistemic uncertainties for neural nets.
Nikita Kotelevskii, Alexander Fishkov, Kirill Fedyanin, Aleksandr Petiushko Александр Петюшко, Maxim Panov
PDF Cite arXiv OpenReview Conf WorkshopWeb
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
MDMMT: Multidomain Multimodal Transformer for Video Retrieval
A State-of-the-Art Video Retrieval Method.
Maksim Dzabraev, Maksim Kalashnikov, Stepan Komkov, Aleksandr Petiushko Александр Петюшко
PDF Cite Code Slides Video Source Document DOI arXiv WorkshopWeb
MDMMT: Multidomain Multimodal Transformer for Video Retrieval
AdvHat: Real-World Adversarial Attack on ArcFace Face ID System
A practical approach to generate forehand printed patch to fool the best FaceID system - Arcface - in real world.
Stepan Komkov, Aleksandr Petiushko Александр Петюшко
PDF Cite Code Poster Slides Video Source Document DOI arXiv Demo
AdvHat: Real-World Adversarial Attack on ArcFace Face ID System