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CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model., , , , , и . EMNLP, стр. 9612-9637. Association for Computational Linguistics, (2023)Towards Building Specialized Generalist AI with System 1 and System 2 Fusion., , и . CoRR, (2024)Enhancing Adversarial Transferability via Information Bottleneck Constraints., , , , и . IEEE Signal Process. Lett., (2024)Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability., , , , , , и . CoRR, (2024)Exploring Adversarial Robustness of Deep State Space Models., , , , , , и . CoRR, (2024)DOA Estimation under GNSS Spoofing Attacks Using a Coprime Array: From a Sparse Reconstruction Viewpoint., , , и . Remote. Sens., 14 (23): 5944 (декабря 2022)Online DPO: Online Direct Preference Optimization with Fast-Slow Chasing., , , , , и . CoRR, (2024)PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuning., , , , , и . NAACL-HLT, стр. 2571-2597. Association for Computational Linguistics, (2024)SMR: State Memory Replay for Long Sequence Modeling., , , , , , и . ACL (Findings), стр. 8102-8116. Association for Computational Linguistics, (2024)Neural Residual Diffusion Models for Deep Scalable Vision Generation., , , и . CoRR, (2024)