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Understanding the Impact of Dynamic Channel Pruning on Conditionally Parameterized Convolutions., , , and . AIChallengeIoT@SenSys, page 27-33. ACM, (2020)Skipping RNN State Updates without Retraining the Original Model., , , and . SenSys-ML, page 31-36. ACM, (2019)Ternary MobileNets via Per-Layer Hybrid Filter Banks., , , and . CoRR, (2019)Efficiently Adapting Pretrained Language Models To New Languages., , , and . CoRR, (2023)Run-Time Efficient RNN Compression for Inference on Edge Devices., , , , and . EMC2@HPCA/CVPR/ISCA, page 26-30. IEEE, (2019)A Survey on Federated Learning for Resource-Constrained IoT Devices., , , , and . IEEE Internet Things J., 9 (1): 1-24 (2022)Measuring scheduling efficiency of RNNs for NLP applications., , , and . CoRR, (2019)PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts., , , , , , , , , and 16 other author(s). CoRR, (2022)PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts., , , , , , , , , and 17 other author(s). ACL (demo), page 93-104. Association for Computational Linguistics, (2022)Compressing RNNs for IoT devices by 15-38x using Kronecker Products., , , , , , and . CoRR, (2019)