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Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities

, , and . (2022)cite arxiv:2210.06640Comment: 77 pages.

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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 . (2022)cite arxiv:2210.06640Comment: 77 pages.Models Out of Line: A Fourier Lens on Distribution Shift Robustness., , , , and . CoRR, (2022)NEFTune: Noisy Embeddings Improve Instruction Finetuning., , , , , , , , , and 3 other author(s). CoRR, (2023)Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities., , and . J. Mach. Learn. Res., (2023)Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion., , , and . CoRR, (2024)Latent Space Simulation for Carbon Capture Design Optimization., , , , , , , and . AAAI, page 12447-12453. AAAI Press, (2022)The Generalization-Stability Tradeoff In Neural Network Pruning., , , and . NeurIPS, (2020)Transformers Can Do Arithmetic with the Right Embeddings., , , , , , , , , and 1 other author(s). CoRR, (2024)NEFTune: Noisy Embeddings Improve Instruction Finetuning., , , , , , , , , and 3 other author(s). ICLR, OpenReview.net, (2024)