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Effective Backdoor Mitigation Depends on the Pre-training Objective.

, , , , , , , и . CoRR, (2023)

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Salomon Schwarzschild

Friedrich Schwarzschild

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Martin Schwarzschild

Albertas Julijonas Aviza

 

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Truth or backpropaganda? An empirical investigation of deep learning theory., , , , и . ICLR, OpenReview.net, (2020)Universal Guidance for Diffusion Models., , , , , , и . CVPR Workshops, стр. 843-852. IEEE, (2023)Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses., , , , , , , , и . CoRR, (2020)Headless Horseman: Adversarial Attacks on Transfer Learning Models., , , , , , , и . ICASSP, стр. 3087-3091. IEEE, (2020)Benchmarking ChatGPT on Algorithmic Reasoning., , и . CoRR, (2024)Adversarial Attacks on Machine Learning Systems for High-Frequency Trading., , , , , и . CoRR, (2020)End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking., , , , , , и . NeurIPS, (2022)Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks., , , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 9389-9398. PMLR, (2021)Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective., , , , и . AIES, стр. 662-678. ACM, (2023)SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training., , , , и . CoRR, (2021)