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Retrieval Augmented Generation (RAG) is emerging as a flexible and robust technique to adapt models to private users data without training, to handle credit attribution, and to allow efficient machine unlearning at scale.
Multi-concept customization of text-to-image diffusion
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The right to be forgotten in the digital age: The challenges of data protection beyond borders
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Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang · 2020
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Denoising diffusion probabilistic models
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The legal frameworks of the right to request the deletion of personal data in the eu, the us and japan and the right to be forgotten: A study focusing on search businesses
Mika Nakashima · 2020
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Machine unlearning
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Extracting training data from large language models
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Diffusion models beat gans on image synthesis
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Murat A Erdogdu and Rasa Hosseinzadeh · 2021
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Mixed-privacy forgetting in deep networks
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Lora: Low-rank adaptation of large language models
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Label-retrieval-augmented diffusion models for learning from noisy labels
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Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and mcmc
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Erasing concepts from diffusion models
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Training data protection with compositional diffusion models
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ediffi: Text-to-image diffusion models with an ensemble of expert denoisers
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Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, and Stefano Soatto · 2022
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Aditya Golatkar, Alessandro Achille, Ashwin Swaminathan, and Stefano Soatto · 2023
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Tian Yu Liu, Aditya Golatkar, and Stefano Soatto · 2023
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Shilin Lu, Yanzhu Liu, and Adams Wai-Kin Kong · 2023
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Jian Ma, Junhao Liang, Chen Chen, and Haonan Lu · 2023
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Silo language models: Isolating legal risk in a nonparametric datastore
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Shawn Shan, Jenna Cryan, Emily Wenger, Haitao Zheng, Rana Hanocka, and Ben Y Zhao · 2023
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Replug: Retrieval-augmented black-box language models
Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih · 2023
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Visual prompt tuning for generative transfer learning
Kihyuk Sohn, Huiwen Chang, José Lezama, Luisa Polania, Han Zhang, Yuan Hao, Irfan Essa, and Lu Jiang · 2023
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Provable copyright protection for generative models
Nikhil Vyas, Sham Kakade, and Boaz Barak · 2023
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Compositional text-to-image synthesis with attention map control of diffusion models
Ruichen Wang, Zekang Chen, Chen Chen, Jian Ma, Haonan Lu, and Xiaodong Lin · 2023
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Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery
Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
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Retrieval-augmented multimodal language modeling
Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Richard James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih · 2023
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Remodiffuse: Retrieval-augmented motion diffusion model
Mingyuan Zhang, Xinying Guo, Liang Pan, Zhongang Cai, Fangzhou Hong, Huirong Li, Lei Yang, and Ziwei Liu · 2023
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