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With evolving data regulations, machine unlearning (MU) has become an important tool for fostering trust and safety in today's AI models.
Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Proximal algorithms
Neal Parikh, Stephen Boyd, et al · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
Song Han, Huizi Mao, and William J Dally · 2015
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Learning deep features for discriminative localization
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2016
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Grad-CAM: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks
Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N Balasubramanian · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
Earlier work this paper cites.
Rise: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
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Nudenet: Neural nets for nudity classification, detection and selective censoring, 2019
P Bedapudi · 2019
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Making ai forget you: Data deletion in machine learning
Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou · 2019
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Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten · 2019
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The european union general data protection regulation: what it is and what it means
Chris Jay Hoofnagle, Bart van der Sloot, and Frederik Zuiderveen Borgesius · 2019
Cited alongside, same era.
Eternal sunshine of the spotless net: Selective forgetting in deep networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
Cited alongside, same era.
Fastai: A layered api for deep learning
Jeremy Howard and Sylvain Gugger · 2020
Cited alongside, same era.
How can i explain this to you? an empirical study of deep neural network explanation methods
Jeya Vikranth Jeyakumar, Joseph Noor, Yu-Hsi Cheng, Luis Garcia, and Mani Srivastava · 2020
Cited alongside, same era.
Multimodal datasets: misogyny, pornography, and malignant stereotypes
Abeba Birhane, Vinay Uday Prabhu, and Emmanuel Kahembwe · 2021
Cited alongside, same era.
Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
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Unrolling sgd: Understanding factors influencing machine unlearning
Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, and Nicolas Papernot · 2022
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Federated unlearning via class-discriminative pruning
Junxiao Wang, Song Guo, Xin Xie, and Heng Qi · 2022
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Federated unlearning: Guarantee the right of clients to forget
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Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei · 2021
Cited alongside, same era.
Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov · 2021
Cited alongside, same era.
Amnesiac machine learning
Laura Graves, Vineel Nagisetty, and Vijay Ganesh · 2021
Cited alongside, same era.
Approximate data deletion from machine learning models
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Cited alongside, same era.
Descent-to-delete: Gradient-based methods for machine unlearning
Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi · 2021
Cited alongside, same era.
Scaling vision with sparse mixture of experts
Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, and Neil Houlsby · 2021
Cited alongside, same era.
Leijie Wu, Song Guo, Junxiao Wang, Zicong Hong, Jie Zhang, and Yaohong Ding · 2022
Later among the works it cites.
Gradient surgery for one-shot unlearning on generative model, 2023
Seohui Bae, Seoyoon Kim, Hyemin Jung, and Woohyung Lim · 2023
Closest in time.
Boundary unlearning: Rapid forgetting of deep networks via shifting the decision boundary
Min Chen, Weizhuo Gao, Gaoyang Liu, Kai Peng, and Chen Wang · 2023
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Gnndelete: A general strategy for unlearning in graph neural networks
Jiali Cheng, George Dasoulas, Huan He, Chirag Agarwal, and Marinka Zitnik · 2023
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Erasing concepts from diffusion models
Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman, and David Bau · 2023
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Studying large language model generalization with influence functions
Roger Grosse, Juhan Bae, Cem Anil, Nelson Elhage, Alex Tamkin, Amirhossein Tajdini, Benoit Steiner, Dustin Li, Esin Durmus, Ethan Perez, et al · 2023
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Selective amnesia: A continual learning approach to forgetting in deep generative models, 2023
Alvin Heng and Harold Soh · 2023
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A data-based perspective on transfer learning
Saachi Jain, Hadi Salman, Alaa Khaddaj, Eric Wong, Sung Min Park, and Aleksander Madry · 2023
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Model sparsification can simplify machine unlearning
Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, and Sijia Liu · 2023
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Towards modular machine learning solution development: Benefits and trade-offs
Samiyuru Menik and Lakshmish Ramaswamy · 2023
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Fair machine unlearning: Data removal while mitigating disparities
Alex Oesterling, Jiaqi Ma, Flavio P Calmon, and Hima Lakkaraju · 2023
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Trak: Attributing model behavior at scale
Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, and Aleksander Madry · 2023
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Can sensitive information be deleted from llms? objectives for defending against extraction attacks
Vaidehi Patil, Peter Hase, and Mohit Bansal · 2023
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Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models, 2023
Patrick Schramowski, Manuel Brack, Björn Deiseroth, and Kristian Kersting · 2023
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Exploring the landscape of machine unlearning: A survey and taxonomy
Thanveer Shaik, Xiaohui Tao, Haoran Xie, Lin Li, Xiaofeng Zhu, and Qing Li · 2023
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Diffusion art or digital forgery? investigating data replication in diffusion models
Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
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Data selection for language models via importance resampling
Sang Michael Xie, Shibani Santurkar, Tengyu Ma, and Percy Liang · 2023
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