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Are Transformers universal approximators of sequence-to-sequence functions?, , , , and . CoRR, (2019)Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks., , , , and . CoRR, (2018)Stochastic Negative Mining for Learning with Large Output Spaces., , , , , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 1940-1949. PMLR, (2019)Baselines for Image Annotation, , and . International Journal of Computer Vision, 90 (1): 88-105 (2010)33\% contribution.Balancing Robustness and Sensitivity using Feature Contrastive Learning., , , , , and . CoRR, (2021)ResMem: Learn what you can and memorize the rest., , , , , , , and . CoRR, (2023)Functional Interpolation for Relative Positions Improves Long Context Transformers., , , , , , , , , and . CoRR, (2023)Federated Learning with Only Positive Labels., , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 10946-10956. PMLR, (2020)Learning discrete distributions: user vs item-level privacy., , , , and . NeurIPS, (2020)Learning Binary Codes for High-Dimensional Data Using Bilinear Projections., , , and . CVPR, page 484-491. IEEE Computer Society, (2013)