Distilled Datamodel with Reverse Gradient Matching

22 April 2024

Songhua Liu

Papers citing "Distilled Datamodel with Reverse Gradient Matching"

40 / 40 papers shown

Title
Diffusion Model as Representation Learner Xingyi Yang Xinchao Wang DiffM 53 56 0 21 Aug 2023
Partial Network Cloning Jingwen Ye Songhua Liu Xinchao Wang CLL 61 14 0 19 Mar 2023
Dataset Distillation: A Comprehensive Review Ruonan Yu Songhua Liu Xinchao Wang DD 67 125 0 17 Jan 2023
Dataset Distillation via Factorization Songhua Liu Kai Wang Xingyi Yang Jingwen Ye Xinchao Wang DD 180 143 0 30 Oct 2022
If Influence Functions are the Answer, Then What is the Question? Juhan Bae Nathan Ng Alston Lo Marzyeh Ghassemi Roger C. Grosse TDI 50 93 0 12 Sep 2022
Dataset Distillation using Neural Feature Regression Yongchao Zhou E. Nezhadarya Jimmy Ba DD FedML 65 155 0 01 Jun 2022
Privacy for Free: How does Dataset Condensation Help Privacy? Tian Dong Bo Zhao Lingjuan Lyu DD 54 115 0 01 Jun 2022
Dataset Condensation with Contrastive Signals Saehyung Lee Sanghyuk Chun Sangwon Jung Sangdoo Yun Sung-Hoon Yoon DD 39 98 0 07 Feb 2022
Datamodels: Predicting Predictions from Training Data Andrew Ilyas Sung Min Park Logan Engstrom Guillaume Leclerc Aleksander Madry TDI 103 136 0 01 Feb 2022
Scaling Up Influence Functions Andrea Schioppa Polina Zablotskaia David Vilar Artem Sokolov TDI 69 96 0 06 Dec 2021
ModelPred: A Framework for Predicting Trained Model from Training Data Yingyan Zeng Jiachen T. Wang Si-An Chen H. Just Ran Jin R. Jia TDI MU 53 3 0 24 Nov 2021
Dataset Distillation with Infinitely Wide Convolutional Networks Timothy Nguyen Roman Novak Lechao Xiao Jaehoon Lee DD 79 233 0 27 Jul 2021
DeepObliviate: A Powerful Charm for Erasing Data Residual Memory in Deep Neural Networks Yingzhe He Guozhu Meng Kai Chen Jinwen He Xingbo Hu MU 28 27 0 13 May 2021
Remember What You Want to Forget: Algorithms for Machine Unlearning Ayush Sekhari Jayadev Acharya Gautam Kamath A. Suresh FedML MU 61 293 0 04 Mar 2021
Zero-Shot Text-to-Image Generation Aditya A. Ramesh Mikhail Pavlov Gabriel Goh Scott Gray Chelsea Voss Alec Radford Mark Chen Ilya Sutskever VLM 329 4,873 0 24 Feb 2021
Dataset Condensation with Differentiable Siamese Augmentation Bo Zhao Hakan Bilen DD 247 300 0 16 Feb 2021
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging Han Guo Nazneen Rajani Peter Hase Joey Tianyi Zhou Caiming Xiong TDI 73 107 0 31 Dec 2020
Variational Bayesian Unlearning Q. Nguyen Bryan Kian Hsiang Low Patrick Jaillet BDL MU 46 124 0 24 Oct 2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai ... Matthias Minderer G. Heigold Sylvain Gelly Jakob Uszkoreit N. Houlsby ViT 425 40,217 0 22 Oct 2020
Machine Unlearning for Random Forests Jonathan Brophy Daniel Lowd MU 43 159 0 11 Sep 2020
Dataset Condensation with Gradient Matching Bo Zhao Konda Reddy Mopuri Hakan Bilen DD 107 489 0 10 Jun 2020
Language Models are Few-Shot Learners Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan ... Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever Dario Amodei BDL 529 41,106 0 28 May 2020
Estimating Training Data Influence by Tracing Gradient Descent G. Pruthi Frederick Liu Mukund Sundararajan Satyen Kale TDI 56 395 0 19 Feb 2020
Machine Unlearning Lucas Bourtoule Varun Chandrasekaran Christopher A. Choquette-Choo Hengrui Jia Adelin Travers Baiwu Zhang David Lie Nicolas Papernot MU 103 830 0 09 Dec 2019
Soft-Label Dataset Distillation and Text Dataset Distillation Ilia Sucholutsky Matthias Schonlau DD 102 135 0 06 Oct 2019
Making AI Forget You: Data Deletion in Machine Learning Antonio A. Ginart M. Guan Gregory Valiant James Zou MU 60 467 0 11 Jul 2019
Generalized Linear Rule Models Dennis L. Wei S. Dash Tian Gao Oktay Gunluk 31 63 0 05 Jun 2019
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin Ming-Wei Chang Kenton Lee Kristina Toutanova VLM SSL SSeg 1.1K 93,936 0 11 Oct 2018
Explanations of model predictions with live and breakDown packages M. Staniak P. Biecek FAtt 13 117 0 05 Apr 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 170 1,828 0 30 Nov 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 651 21,613 0 22 May 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 142 2,854 0 14 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 142 5,920 0 04 Mar 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 225 19,796 0 07 Oct 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 681 16,828 0 16 Feb 2016
Learning Deep Features for Discriminative Localization Bolei Zhou A. Khosla Àgata Lapedriza A. Oliva Antonio Torralba SSL SSeg FAtt 186 9,280 0 14 Dec 2015
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.5K 192,638 0 10 Dec 2015
Supersparse Linear Integer Models for Optimized Medical Scoring Systems Berk Ustun Cynthia Rudin 71 352 0 15 Feb 2015
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 1.2K 39,383 0 01 Sep 2014
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 376 15,825 0 12 Nov 2013