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LIT: Learned Intermediate Representation Training for Model Compression., , , и . ICML, том 97 из Proceedings of Machine Learning Research, стр. 3509-3518. PMLR, (2019)TAOBench: An End-to-End Benchmark for Social Networking Workloads., , , , , , , , , и 3 other автор(ы). Proc. VLDB Endow., 15 (9): 1965-1977 (2022)Accelerating Approximate Aggregation Queries with Expensive Predicates., , , , , и . Proc. VLDB Endow., 14 (11): 2341-2354 (2021)Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark., , , , , , , , , и . ACM SIGOPS Oper. Syst. Rev., 53 (1): 14-25 (2019)Contracting Wide-area Network Topologies to Solve Flow Problems Quickly., , , , , и . NSDI, стр. 175-200. USENIX Association, (2021)Infrastructure for Usable Machine Learning: The Stanford DAWN Project, , , и . (2017)cite arxiv:1705.07538.DROP: A Workload-Aware Optimizer for Dimensionality Reduction., и . DEEM@SIGMOD, стр. 1:1-1:10. ACM, (2019)Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data., , , и . ICML, том 97 из Proceedings of Machine Learning Research, стр. 5690-5700. PMLR, (2019)Equivariant Transformer Networks., , и . ICML, том 97 из Proceedings of Machine Learning Research, стр. 6086-6095. PMLR, (2019)Coordination Avoidance in Database Systems (Extended Version), , , , , и . (2014)cite arxiv:1402.2237Comment: Extended version of paper appearing in PVLDB Vol. 8, No. 3.