| {"paper_url": "https://huggingface.co/papers/2112.05682", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [CAST: Clustering Self-Attention using Surrogate Tokens for Efficient Transformers](https://huggingface.co/papers/2402.04239) (2024)\n* [AutoChunk: Automated Activation Chunk for Memory-Efficient Long Sequence Inference](https://huggingface.co/papers/2401.10652) (2024)\n* [TaylorShift: Shifting the Complexity of Self-Attention from Squared to Linear (and Back) using Taylor-Softmax](https://huggingface.co/papers/2403.02920) (2024)\n* [Faster Neighborhood Attention: Reducing the O(n^2) Cost of Self Attention at the Threadblock Level](https://huggingface.co/papers/2403.04690) (2024)\n* [NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attention](https://huggingface.co/papers/2403.01273) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"} |