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STYX: Adaptive Poisoning Attacks Against Byzantine-Robust Defenses in Federated Learning., , , и . ICASSP, стр. 1-5. IEEE, (2023)SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training., , , , и . CoRR, (2021)MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data., , , , , и . CoRR, (2021)NEFTune: Noisy Embeddings Improve Instruction Finetuning., , , , , , , , , и 3 other автор(ы). CoRR, (2023)Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery., , , , , и . CoRR, (2023)Adversarially Robust Distillation., , , и . CoRR, (2019)Witchcraft: Efficient PGD Attacks with Random Step Size., , , , , , и . ICASSP, стр. 3747-3751. IEEE, (2020)Adversarial Examples Make Strong Poisons., , , , , и . NeurIPS, стр. 30339-30351. (2021)Adversarial attacks on machine learning systems for high-frequency trading., , , и . ICAIF, стр. 2:1-2:9. ACM, (2021)Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation., , , , и . ICLR, OpenReview.net, (2023)