Grabovoy Andrey Valerievich

Publications in Math-Net.Ru

1. Interpreting transformer-based classifiers via clustering
   
   Dokl. RAN. Math. Inf. Proc. Upr., 527 (2025),  432–448
2. Dynamic division of labor in hybrid AI: contrasting encoder strategies and their impact on LSTM modulators
   
   Dokl. RAN. Math. Inf. Proc. Upr., 527 (2025),  117–133
3. Beyond familiar domains: a study of the generalization capability of machine-generated image detectors
   
   Dokl. RAN. Math. Inf. Proc. Upr., 527 (2025),  103–116
4. Pairwise image matching for plagiarism detection
   
   Dokl. RAN. Math. Inf. Proc. Upr., 527 (2025),  68–83
5. Enhancing fMRI data decoding with spatiotemporal characteristics in limited dataset
   
   Dokl. RAN. Math. Inf. Proc. Upr., 527 (2025),  11–30
6. Sample size determination: likelihood bootstrapping
   
   Zh. Vychisl. Mat. Mat. Fiz., 65:2 (2025),  235–242
7. Stack more LLMs: efficient detection of machine-generated texts via perplexity approximation
   
   Dokl. RAN. Math. Inf. Proc. Upr., 520:2 (2024),  228–237
8. Unraveling the Hessian: a key to smooth convergence in loss function landscapes
   
   Dokl. RAN. Math. Inf. Proc. Upr., 520:2 (2024),  57–70
9. Artificially generated text fragments search in academic documents
   
   Dokl. RAN. Math. Inf. Proc. Upr., 514:2 (2023),  308–317
10. Text reuse detection in handwritten documents
    
    Dokl. RAN. Math. Inf. Proc. Upr., 514:2 (2023),  297–307
11. Analysis of the properties of probabilistic models in expert-augmented learning problems
    
    Avtomat. i Telemekh., 2022, no. 10,  47–59
12. Probabilistic interpretation of the distillation problem
    
    Avtomat. i Telemekh., 2022, no. 1,  150–168
13. Bayesian distillation of deep learning models
    
    Avtomat. i Telemekh., 2021, no. 11,  16–29
14. Prior distribution selection for a mixture of experts
    
    Zh. Vychisl. Mat. Mat. Fiz., 61:7 (2021),  1149–1161
15. Ordering the set of neural network parameters
    
    Inform. Primen., 14:2 (2020),  58–65
16. Estimation of the relevance of the neural network parameters
    
    Inform. Primen., 13:2 (2019),  62–70