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Revisiting Automated Prompting: Are We Actually Doing Better?

, , , , and . ACL (2), page 1822-1832. Association for Computational Linguistics, (2023)

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Efficient and Effective Quantization for Sparse DNNs., , , , and . CoRR, (2019)Revisiting Embeddings for Graph Neural Networks., , and . CoRR, (2022)Efficient Adversarial Training With Data Pruning., , , , and . CoRR, (2022)DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning., , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 8852-8866. PMLR, (2022)Automatic Generation of Multi-Precision Multi-Arithmetic CNN Accelerators for FPGAs., , , , , , , , and . FPT, page 45-53. IEEE, (2019)Micronets: a model for decentralising control in asynchronous processor architectures., , and . ASYNC, page 190-199. IEEE Computer Society, (1995)Trace-and-brace (TAB): bespoke software countermeasures against soft errors., , , and . LCTES, page 73-85. ACM, (2022)Dynamic Channel Pruning: Feature Boosting and Suppression., , , , and . CoRR, (2018)Characterizing Sources of Ineffectual Computations in Deep Learning Networks., , , , and . ISPASS, page 165-176. IEEE, (2019)ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks., , , , and . SaTML, page 344-357. IEEE, (2024)