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RegNet: Self-Regulated Network for Image Classification.

, , , , , and . IEEE Trans. Neural Networks Learn. Syst., 34 (11): 9562-9567 (November 2023)

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PipeMoE: Accelerating Mixture-of-Experts through Adaptive Pipelining., , , and . INFOCOM, page 1-10. IEEE, (2023)ENIGMA: Low-Latency and Privacy-Preserving Edge Inference on Heterogeneous Neural Network Accelerators., , , , and . ICDCS, page 458-469. IEEE, (2022)Parm: Efficient Training of Large Sparsely-Activated Models with Dedicated Schedules., , , , , and . INFOCOM, page 1880-1889. IEEE, (2024)Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models., , , , , , , , , and 1 other author(s). CoRR, (2023)Should We Really Edit Language Models? On the Evaluation of Edited Language Models., , , , , , and . NeurIPS, (2024)CORE: CORrelation-Guided Feature Enhancement for Few-Shot Image Classification., , , , , and . IEEE Trans. Neural Networks Learn. Syst., 36 (2): 3098-3110 (February 2025)FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models., , , , , , , and . ASPLOS (1), page 524-539. ACM, (2025)ScheMoE: An Extensible Mixture-of-Experts Distributed Training System with Tasks Scheduling., , , , , , , , and . EuroSys, page 236-249. ACM, (2024)Generalized Category Discovery with Clustering Assignment Consistency., , , and . ICONIP (5), volume 14451 of Lecture Notes in Computer Science, page 535-547. Springer, (2023)Pruner-Zero: Evolving Symbolic Pruning Metric From Scratch for Large Language Models., , , , , , and . ICML, OpenReview.net, (2024)