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Recent advancements in large language models (LLMs) have indeed showcased their impressive capabilities.
Multivariate stochastic approximation using a simultaneous perturbation gradient approximation
James C Spall. 1992 · 1992
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche. 2017 · 2017
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A first look at deep learning apps on smartphones
Mengwei Xu, Jiawei Liu, Yuanqiang Liu, Felix Xiaozhu Lin, Yunxin Liu, and Xuanzhe Liu. 2019 · 2019
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Edge intelligence: Paving the last mile of artificial intelligence with edge computing
Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang. 2019 · 2019
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Attacking and protecting data privacy in edge–cloud collaborative inference systems
Zecheng He, Tianwei Zhang, and Ruby B Lee. 2020 · 2020
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Smart at what cost? characterising mobile deep neural networks in the wild
Mario Almeida, Stefanos Laskaridis, Abhinav Mehrotra, Lukasz Dudziak, Ilias Leontiadis, and Nicholas D Lane. 2021 · 2021
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Lora: Low-rank adaptation of large language models
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{ \{ Zero-offload } \} : Democratizing { \{ billion-scale } \} model training
Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, and Yuxiong He. 2021 · 2021
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Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. 2022 · 2022
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Edge-cloud polarization and collaboration: A comprehensive survey for ai
Jiangchao Yao, Shengyu Zhang, Yang Yao, Feng Wang, Jianxin Ma, Jianwei Zhang, Yunfei Chu, Luo Ji, Kunyang Jia, Tao Shen, et al. 2022 · 2022
Cited alongside, same era.
Full parameter fine-tuning for large language models with limited resources
Kai Lv, Yuqing Yang, Tengxiao Liu, Qinghui Gao, Qipeng Guo, and Xipeng Qiu. 2023 · 2023
Later among the works it cites.
Llm-pruner: On the structural pruning of large language models
Xinyin Ma, Gongfan Fang, and Xinchao Wang. 2023 · 2023
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Lora-fa: Memory-efficient low-rank adaptation for large language models fine-tuning
Longteng Zhang, Lin Zhang, Shaohuai Shi, Xiaowen Chu, and Bo Li. 2023 · 2023
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Pockengine: Sparse and efficient fine-tuning in a pocket
Ligeng Zhu, Lanxiang Hu, Ji Lin, Wei-Ming Chen, Wei-Chen Wang, Chuang Gan, and Song Han. 2023 · 2023
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Personal llm agents: Insights and survey about the capability, efficiency and security
Yuanchun Li, Hao Wen, Weijun Wang, Xiangyu Li, Yizhen Yuan, Guohong Liu, Jiacheng Liu, Wenxing Xu, Xiang Wang, Yi Sun, Rui Kong, Yile Wang, Hanfei Geng, Jian Luan, Xuefeng Jin, Zilong Ye, Guanjing Xiong, Fan Zhang, Xiang Li, Mengwei Xu, Zhijun Li, Peng Li, Yang Liu, Ya-Qin Zhang, and Yunxin Liu. 2024 · 2024
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Yihan Cao, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai, Philip S Yu, and Lichao Sun. 2023 · 2023
Cited alongside, same era.
Parameter-efficient fine-tuning of large-scale pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al. 2023 · 2023
Cited alongside, same era.
Make your pre-trained model reversible: From parameter to memory efficient fine-tuning
Baohao Liao, Shaomu Tan, and Christof Monz. 2023 · 2023
Cited alongside, same era.
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Fine-tuning language models with just forward passes
Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D Lee, Danqi Chen, and Sanjeev Arora. 2024 · 2024
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Iot in the era of generative ai: Vision and challenges
Xin Wang, Zhongwei Wan, Arvin Hekmati, Mingyu Zong, Samiul Alam, Mi Zhang, and Bhaskar Krishnamachari. 2024 · 2024
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