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Quantized Convolutional Neural Networks for Mobile Devices.

, , , , and . CVPR, page 4820-4828. IEEE Computer Society, (2016)

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AirFace: Lightweight and Efficient Model for Face Recognition, , , and . (2019)cite arxiv:1907.12256.DeepSearch: A Fast Image Search Framework for Mobile Devices., , , , and . TOMM, 14 (1): 6:1-6:22 (2018)ATF: An Alternating Training Framework for Weakly Supervised Face Alignment., , and . IEEE Trans. Multim., (2023)InternEvo: Efficient Long-sequence Large Language Model Training via Hybrid Parallelism and Redundant Sharding., , , , , , , , , and 1 other author(s). CoRR, (2024)Quantized Convolutional Neural Networks for Mobile Devices., , , , and . CoRR, (2015)Quantized CNN: A Unified Approach to Accelerate and Compress Convolutional Networks., , , , and . IEEE Trans. Neural Networks Learn. Syst., 29 (10): 4730-4743 (2018)Hydro: Surrogate-Based Hyperparameter Tuning Service in Datacenters., , , , , , and . OSDI, page 757-777. USENIX Association, (2023)Accumulated Trivial Attention Matters in Vision Transformers on Small Datasets., , , , and . WACV, page 3973-3981. IEEE, (2023)Pseudo Label based Unsupervised Deep Discriminative Hashing for Image Retrieval., , , , and . ACM Multimedia, page 1584-1590. ACM, (2017)A2Q: Aggregation-Aware Quantization for Graph Neural Networks., , , , , , , and . CoRR, (2023)