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Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators.

, , , , , и . ICLR, OpenReview.net, (2022)

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cuDNN: Efficient Primitives for Deep Learning., , , , , , и . CoRR, (2014)Mellotron: Multispeaker Expressive Voice Synthesis by Conditioning on Rhythm, Pitch and Global Style Tokens., , , и . ICASSP, стр. 6189-6193. IEEE, (2020)Graphical Contrastive Losses for Scene Graph Generation., , , , и . CoRR, (2019)One TTS Alignment to Rule Them All., , , , , и . ICASSP, стр. 6092-6096. IEEE, (2022)Any-to-Any Voice Conversion with F0 and Timbre Disentanglement and Novel Timbre Conditioning., , , и . ICASSP, стр. 1-5. IEEE, (2023)Panoptic-Based Image Synthesis., , , , и . CVPR, стр. 8067-8076. Computer Vision Foundation / IEEE, (2020)Evaluating Parameter Efficient Learning for Generation., , , , , , , , и . EMNLP, стр. 4824-4833. Association for Computational Linguistics, (2022)Higher Radix Floating-Point Representations for FPGA-Based Arithmetic., и . FCCM, стр. 161-170. IEEE Computer Society, (2005)Persistent RNNs: Stashing Recurrent Weights On-Chip., , , , , , , , и . ICML, том 48 из JMLR Workshop and Conference Proceedings, стр. 2024-2033. JMLR.org, (2016)Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning., , , , , , , , , и 6 other автор(ы). EMNLP (Findings), стр. 11844-11857. Association for Computational Linguistics, (2023)