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Long-context Multimodal Large Language Models (MLLMs) demand substantial computational resources for inference as the growth of their multimodal Key-Value (KV) cache, in response to increasing input lengths, challenges memory and time efficiency.
Large scale many-objective optimization driven by distributional adversarial networks
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Many-objective estimation of distribution optimization algorithm based on wgan-gp
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Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman. 2022 · 2022
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Earlier work this paper cites.
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Jing Xiong, Zhongwei Wan, Xiping Hu, Min Yang, and Chengming Li. 2022 · 2022
Earlier work this paper cites.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
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Qingqing Cao, Bhargavi Paranjape, and Hannaneh Hajishirzi. 2023 · 2023
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Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks
Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Zhong Muyan, Qinglong Zhang, Xizhou Zhu, Lewei Lu, Bin Li, Ping Luo, Tong Lu, Yu Qiao, and Jifeng Dai. 2023 · 2023
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Model tells you what to discard: Adaptive kv cache compression for llms
Suyu Ge, Yunan Zhang, Liyuan Liu, Minjia Zhang, Jiawei Han, and Jianfeng Gao. 2023 · 2023
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
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Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023 · 2023
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Get more with less: Synthesizing recurrence with kv cache compression for efficient llm inference
Harry Dong, Xinyu Yang, Zhenyu Zhang, Zhangyang Wang, Yuejie Chi, and Beidi Chen. 2024 · 2024
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Gear: An efficient kv cache compression recipefor near-lossless generative inference of llm
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Snapkv: Llm knows what you are looking for before generation
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Introducing meta llama 3: The most capable openly available llm to date
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Dynamic token pruning in plain vision transformers for semantic segmentation
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Efficient streaming language models with attention sinks
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Dynamic memory compression: Retrofitting llms for accelerated inference
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