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Stochastic Negative Mining for Learning with Large Output Spaces., , , , , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 1940-1949. PMLR, (2019)Are Transformers universal approximators of sequence-to-sequence functions?, , , , and . CoRR, (2019)Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing., , , , and . CoRR, (2015)Federated Composite Optimization., , and . CoRR, (2020)In defense of dual-encoders for neural ranking., , , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 15376-15400. PMLR, (2022)Efficient Document Ranking with Learnable Late Interactions., , , , , , , , and . CoRR, (2024)Fast stochastic optimization on Riemannian manifolds., , and . CoRR, (2016)Why ADAM Beats SGD for Attention Models., , , , , , and . CoRR, (2019)Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets., , , , and . CoRR, (2020)O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers., , , , , and . NeurIPS, (2020)