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MGSim + MGMark: A Framework for Multi-GPU System Research., , , , , , , , , and 3 other author(s). CoRR, (2018)Active Sets Improves Learning for Mixture Models., and . CoRR, (2015)Feature Selection Facilitates Learning Mixtures of Discrete Product Distributions., and . CoRR, (2017)MGPUSim: enabling multi-GPU performance modeling and optimization., , , , , , , , , and 8 other author(s). ISCA, page 197-209. ACM, (2019)Flan-MoE: Scaling Instruction-Finetuned Language Models with Sparse Mixture of Experts., , , , , , , , , and 10 other author(s). CoRR, (2023)Rethinking the Role of Token Retrieval in Multi-Vector Retrieval., , , , , , and . CoRR, (2023)Attributed Text Generation via Post-hoc Research and Revision., , , , , , , , , and 1 other author(s). CoRR, (2022)Scaling Instruction-Finetuned Language Models., , , , , , , , , and 21 other author(s). CoRR, (2022)Mixture-of-Experts with Expert Choice Routing., , , , , , , , , and . NeurIPS, (2022)Finetuned Language Models Are Zero-Shot Learners., , , , , , , , and . CoRR, (2021)