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Transfer Understanding from Head Queries to Tail Queries., , , и . CIKM, стр. 1299-1308. ACM, (2014)Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models., , , , , , , и . CoRR, (2023)Competition-Level Problems are Effective LLM Evaluators., , , , , , , , , и 1 other автор(ы). CoRR, (2023)LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models., , , , , , и . CoRR, (2023)MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation., , , , , и . NAACL-HLT, стр. 1610-1623. Association for Computational Linguistics, (2022)OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering., , , , и . NAACL-HLT, стр. 932-942. Association for Computational Linguistics, (2022)GRILL: Grounded Vision-language Pre-training via Aligning Text and Image Regions., , , , , , , и . CoRR, (2023)GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation., , , , , , и . CoRR, (2022)BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining., , , , , , , , , и 2 other автор(ы). CoRR, (2020)Mixing and Shifting: Exploiting Global and Local Dependencies in Vision MLPs., , , и . CoRR, (2022)