DECAF: De-Clustering for Adaptive Representational Unlearning | Paper Citation Record

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DECAF: De-Clustering for Adaptive Representational Unlearning S&P , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning IEEE Transactions on Information Forensics and Security , volume=

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DECAF: De-Clustering for Adaptive Representational Unlearning NeurIPS , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning S&P , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

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DECAF: De-Clustering for Adaptive Representational Unlearning Extracting Training Data from Large Language Models

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DECAF: De-Clustering for Adaptive Representational Unlearning Proceedings of the 2021 ACM conference on fairness, accountability, and transparency , pages=

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DECAF: De-Clustering for Adaptive Representational Unlearning 2021 , note=

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DECAF: De-Clustering for Adaptive Representational Unlearning IEEE Transactions on Neural Networks and Learning Systems , year=

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This paper cites POUR: A Provably Optimal Method for Unlearning Representations via Neural Collapse.

DECAF: De-Clustering for Adaptive Representational Unlearning POUR: A Provably Optimal Method for Unlearning Representations via Neural Collapse

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DECAF: De-Clustering for Adaptive Representational Unlearning 2018 , url=

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DECAF: De-Clustering for Adaptive Representational Unlearning On the Opportunities and Risks of Foundation Models

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DECAF: De-Clustering for Adaptive Representational Unlearning A Practical Guide, 1st Ed., Cham: Springer International Publishing , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning INFOCOM , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning AISTATS , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning CVPR , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning AAAI , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning ICML , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning Machine Learning , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning ICML , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning Cluster Computing , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning Certifiable Machine Unlearning for Linear Models

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DECAF: De-Clustering for Adaptive Representational Unlearning EuroS&P , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning CVPR , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning NSDI , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning , title =

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DECAF: De-Clustering for Adaptive Representational Unlearning A Survey of Machine Unlearning

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DECAF: De-Clustering for Adaptive Representational Unlearning CVPR , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning AAAI , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning NeurIPS , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning TNNLS , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects

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DECAF: De-Clustering for Adaptive Representational Unlearning AAAI , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning Economics , year=

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DECAF: De-Clustering for Adaptive Representational Unlearning Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models

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DECAF: De-Clustering for Adaptive Representational Unlearning Distilling the Knowledge in a Neural Network

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DECAF: De-Clustering for Adaptive Representational Unlearning FitNets: Hints for Thin Deep Nets

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DECAF: De-Clustering for Adaptive Representational Unlearning DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

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DECAF: De-Clustering for Adaptive Representational Unlearning Progressive Distillation for Fast Sampling of Diffusion Models

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DECAF: De-Clustering for Adaptive Representational Unlearning SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation

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DECAF: De-Clustering for Adaptive Representational Unlearning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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DECAF: De-Clustering for Adaptive Representational Unlearning Very Deep Convolutional Networks for Large-Scale Image Recognition

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DECAF: De-Clustering for Adaptive Representational Unlearning Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models

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DECAF: De-Clustering for Adaptive Representational Unlearning BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain , journal =

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DECAF: De-Clustering for Adaptive Representational Unlearning Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks , booktitle =

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Reference 80

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Reference 93

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