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A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness., , , , and . NeurIPS, page 664-676. (2021)Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities., , and . J. Mach. Learn. Res., (2023)NEFTune: Noisy Embeddings Improve Instruction Finetuning., , , , , , , , , and 3 other author(s). CoRR, (2023)Enhancing the Regularization Effect of Weight Pruning in Artificial Neural Networks., , and . CoRR, (2018)Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities., , and . CoRR, (2022)Models Out of Line: A Fourier Lens on Distribution Shift Robustness., , , , and . CoRR, (2022)Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics., , , , , , , , and . CoRR, (2023)Models Out of Line: A Fourier Lens on Distribution Shift Robustness., , , , and . NeurIPS, (2022)Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression., , , , , , , , , and 5 other author(s). CoRR, (2024)The Generalization-Stability Tradeoff in Neural Network Pruning., , , and . CoRR, (2019)