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Kirill Bykov
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2020 – today
- 2024
- [i9]Dilyara Bareeva, Marina M.-C. Höhne, Alexander Warnecke, Lukas Pirch, Klaus-Robert Müller, Konrad Rieck, Kirill Bykov:
Manipulating Feature Visualizations with Gradient Slingshots. CoRR abs/2401.06122 (2024) - [i8]Laura Kopf, Philine Lou Bommer, Anna Hedström, Sebastian Lapuschkin, Marina M.-C. Höhne, Kirill Bykov:
CoSy: Evaluating Textual Explanations of Neurons. CoRR abs/2405.20331 (2024) - 2023
- [j3]Kirill Bykov, Mayukh Deb, Dennis Grinwald, Klaus-Robert Müller, Marina M.-C. Höhne:
DORA: Exploring Outlier Representations in Deep Neural Networks. Trans. Mach. Learn. Res. 2023 (2023) - [j2]Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina M.-C. Höhne:
Visualizing the Diversity of Representations Learned by Bayesian Neural Networks. Trans. Mach. Learn. Res. 2023 (2023) - [c6]Kirill Bykov
, Klaus-Robert Müller
, Marina M.-C. Höhne
:
Mark My Words: Dangers of Watermarked Images in ImageNet. ECAI Workshops (1) 2023: 426-434 - [c5]Oleg Lashinin, Kirill Bykov, Marina Ananyeva, Sergey Kolesnikov:
GPT3RecBot: a universal chatbot recommender of movies, books and music in Telegram. KaRS@RecSys 2023: 35-43 - [c4]Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne:
Labeling Neural Representations with Inverse Recognition. NeurIPS 2023 - [c3]Kirill Bykov
, Laura Kopf
, Marina M.-C. Höhne
:
Finding Spurious Correlations with Function-Semantic Contrast Analysis. xAI (2) 2023: 549-572 - [i7]Kirill Bykov, Klaus-Robert Müller, Marina M.-C. Höhne:
Mark My Words: Dangers of Watermarked Images in ImageNet. CoRR abs/2303.05498 (2023) - [i6]Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne:
Labeling Neural Representations with Inverse Recognition. CoRR abs/2311.13594 (2023) - 2022
- [j1]Lukas Steiner
, Matthias Jung, Felipe S. Prado, Kirill Bykov, Norbert Wehn:
DRAMSys4.0: An Open-Source Simulation Framework for In-depth DRAM Analyses. Int. J. Parallel Program. 50(2): 217-242 (2022) - [c2]Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne:
NoiseGrad - Enhancing Explanations by Introducing Stochasticity to Model Weights. AAAI 2022: 6132-6140 - [i5]Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina M.-C. Höhne:
Visualizing the diversity of representations learned by Bayesian neural networks. CoRR abs/2201.10859 (2022) - [i4]Kirill Bykov, Mayukh Deb, Dennis Grinwald, Klaus-Robert Müller, Marina M.-C. Höhne:
DORA: Exploring outlier representations in Deep Neural Networks. CoRR abs/2206.04530 (2022) - 2021
- [i3]Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne:
NoiseGrad: enhancing explanations by introducing stochasticity to model weights. CoRR abs/2106.10185 (2021) - [i2]Kirill Bykov, Marina M.-C. Höhne, Adelaida Creosteanu, Klaus-Robert Müller, Frederick Klauschen, Shinichi Nakajima, Marius Kloft:
Explaining Bayesian Neural Networks. CoRR abs/2108.10346 (2021) - 2020
- [c1]Lukas Steiner, Matthias Jung, Felipe S. Prado, Kirill Bykov, Norbert Wehn
:
DRAMSys4.0: A Fast and Cycle-Accurate SystemC/TLM-Based DRAM Simulator. SAMOS 2020: 110-126 - [i1]Kirill Bykov, Marina M.-C. Höhne, Klaus-Robert Müller, Shinichi Nakajima, Marius Kloft:
How Much Can I Trust You? - Quantifying Uncertainties in Explaining Neural Networks. CoRR abs/2006.09000 (2020)
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last updated on 2024-08-02 20:19 CEST by the dblp team
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