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Attention-based Multimodal Neural Machine Translation.

, , , , and . WMT, page 639-645. The Association for Computer Linguistics, (2016)

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UT5: Pretraining Non autoregressive T5 with unrolled denoising., , , and . CoRR, (2023)Gradient-Based Automated Iterative Recovery for Parameter-Efficient Tuning., , , , , and . CoRR, (2023)Chefs' Random Tables: Non-Trigonometric Random Features., , , , , and . NeurIPS, (2022)Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles., , , , , and . NeurIPS, page 14980-14992. (2021)Non-Intrusive Adaptation: Input-Centric Parameter-efficient Fine-Tuning for Versatile Multimodal Modeling., , , , , , , , , and 1 other author(s). CoRR, (2023)SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention., , , , , , , , , and 4 other author(s). ICRA, page 6920-6927. IEEE, (2024)Improving Multilingual and Code-Switching ASR Using Large Language Model Generated Text., , , , , , , and . ASRU, page 1-7. IEEE, (2023)Automata-based constraints for language model decoding., , and . CoRR, (2024)Estimating Training Data Influence by Tracking Gradient Descent., , , and . CoRR, (2020)Attention-based Multimodal Neural Machine Translation., , , , and . WMT, page 639-645. The Association for Computer Linguistics, (2016)