Fetching the paper…
Reading the bibliography…
We present Synergy Aware Forgetting Ensemble (SAFE), a method to adapt large models on a diverse collection of data while minimizing the expected cost to remove the influence of training samples from the trained model.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Recognizing indoor scenes
Ariadna Quattoni and Antonio Torralba · 2009
Earlier work this paper cites.
Novel dataset for fine-grained image categorization
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Li Fei-Fei · 2011
Earlier work this paper cites.
Technical Report CNS-TR-2011-001, California Institute of Technology, 2011
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
Earlier work this paper cites.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Rényi differential privacy
Ilya Mironov · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Earlier work this paper cites.
Making ai forget you: Data deletion in machine learning
Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou · 2019
Earlier work this paper cites.
Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
Earlier work this paper cites.
Semi-supervised domain adaptation via minimax entropy
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, and Kate Saenko · 2019
Earlier work this paper cites.
Pytorch image models
Ross Wightman · 2019
Earlier work this paper cites.
Eternal sunshine of the spotless net: Selective forgetting in deep networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
Earlier work this paper cites.
Forgetting outside the box: Scrubbing deep networks of information accessible from input-output observations
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
Earlier work this paper cites.
Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten · 2020
Earlier work this paper cites.
Gaoyang Liu, Xiaoqiang Ma, Yang Yang, Chen Wang, and Jiangchuan Liu · 2020
Cited alongside, same era.
Deltagrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, and Susan Davidson · 2020
Cited alongside, same era.
Lqf: Linear quadratic fine-tuning
Alessandro Achille, Aditya Golatkar, Avinash Ravichandran, Marzia Polito, and Stefano Soatto · 2021
Cited alongside, same era.
Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Cited alongside, same era.
Palm: Scaling language modeling with pathways, 2022
Aakanksha et al. Chowdhery · 2022
Later among the works it cites.
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer · 2022
Later among the works it cites.
munet: Evolving pretrained deep neural networks into scalable auto-tuning multitask systems
Andrea Gesmundo and Jeff Dean · 2022
Later among the works it cites.
Mixed differential privacy in computer vision
Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, and Stefano Soatto · 2022
Later among the works it cites.
Forget-svgd: Particle-based bayesian federated unlearning
Jinu Gong, Joonhyuk Kang, Osvaldo Simeone, and Rahif Kassab · 2022
Later among the works it cites.
Caltech 256, Apr 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Aditya Golatkar, Alessandro Achille, Avinash Ravichandran, Marzia Polito, and Stefano Soatto · 2021
Cited alongside, same era.
Numerical composition of differential privacy
Sivakanth Gopi, Yin Tat Lee, and Lukas Wutschitz · 2021
Cited alongside, same era.
Adaptive machine unlearning
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites · 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.
Remember what you want to forget: Algorithms for machine unlearning
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, and Ananda Theertha Suresh · 2021
Cited alongside, same era.
Machine unlearning via algorithmic stability
Enayat Ullah, Tung Mai, Anup Rao, Ryan A Rossi, and Raman Arora · 2021
Cited alongside, same era.
Exploring user historical semantic and sentiment preference for microblog sentiment classification
Xiaofei Zhu, Jie Wu, Ling Zhu, Jiafeng Guo, Ran Yu, Katarina Boland, and Stefan Dietze · 2021
Cited alongside, same era.
Griffin, Holub, and Perona · 2022
Later among the works it cites.
Privacy adhering machine un-learning in nlp
Vinayshekhar Bannihatti Kumar, Rashmi Gangadharaiah, and Dan Roth · 2022
Later among the works it cites.
The right to be forgotten in federated learning: An efficient realization with rapid retraining
Yi Liu, Lei Xu, Xingliang Yuan, Cong Wang, and Bo Li · 2022
Later among the works it cites.
Unlearning nonlinear graph classifiers in the limited training data regime
Chao Pan, Eli Chien, and Olgica Milenkovic · 2022
Later among the works it cites.
Unrolling sgd: Understanding factors influencing machine unlearning
Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, and Nicolas Papernot · 2022
Later among the works it cites.
Federated unlearning via class-discriminative pruning
Junxiao Wang, Song Guo, Xin Xie, and Heng Qi · 2022
Later among the works it cites.
Arcane: An efficient architecture for exact machine unlearning
Haonan Yan, Xiaoguang Li, Ziyao Guo, Hui Li, Fenghua Li, and Xiaodong Lin · 2022
Later among the works it cites.
A-la-carte prompt tuning (apt): Combining distinct data via composable prompting
Benjamin Bowman, Alessandro Achille, Luca Zancato, Matthew Trager, Pramuditha Perera, Giovanni Paolini, and Stefano Soatto · 2023
Closest in time.
Fedrecover: Recovering from poisoning attacks in federated learning using historical information
Xiaoyu Cao, Jinyuan Jia, Zaixi Zhang, and Neil Zhenqiang Gong · 2023
Closest in time.
Introspective cross-attention probing for lightweight transfer of pre-trained models
Yonatan Dukler, Alessandro Achille, Hao Yang, Varsha Vivek, Luca Zancato, Ben Bowman, Avinash Ravichandran, Charless Fowlkes, Ashwin Swaminathan, and Stefano Soatto · 2023
Closest in time.
Multipath agents for modular multitask ml systems
Andrea Gesmundo · 2023
Closest in time.
Heterogeneous federated knowledge graph embedding learning and unlearning
Xiangrong Zhu, Guangyao Li, and Wei Hu · 2023
Closest in time.