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Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data., , , , и . ICML, том 80 из Proceedings of Machine Learning Research, стр. 195-204. PMLR, (2018)A Closer Look at the Optimization Landscapes of Generative Adversarial Networks, , , , и . (2019)cite arxiv:1906.04848.UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning., , , , , , , и . ACL (1), стр. 6253-6264. Association for Computational Linguistics, (2022)Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model., , , , , , , , и . CoRR, (2023)Llama 2: Open Foundation and Fine-Tuned Chat Models., , , , , , , , , и 58 other автор(ы). CoRR, (2023)Theano: A Python framework for fast computation of mathematical expressions, , , , , , , , , и 103 other автор(ы). (2016)cite arxiv:1605.02688Comment: 19 pages, 5 figures.Calibrating Energy-based Generative Adversarial Networks., , , , и . ICLR (Poster), OpenReview.net, (2017)Llama 2: Open Foundation and Fine-Tuned Chat Models, , , , , , , , , и 58 other автор(ы). (18.07.2023)Learning Easily Updated General Purpose Text Representations with Adaptable Task-Specific Prefixes., , , , , и . CoRR, (2023)Uniform Masking Prevails in Vision-Language Pretraining., , , , , , и . CoRR, (2022)