Dataset Meta-Learning from Kernel Ridge-Regression

30 October 2020

Papers citing "Dataset Meta-Learning from Kernel Ridge-Regression"

47 / 47 papers shown

Title
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory Mingzhuo Li Guang Li Jiafeng Mao Takahiro Ogawa Miki Haseyama DD 198 0 0 26 May 2025
CONCORD: Concept-Informed Diffusion for Dataset Distillation Jianyang Gu Haonan Wang Ruoxi Jia Saeed Vahidian Vyacheslav Kungurtsev Wei Jiang Yiran Chen DiffM DD 878 0 0 23 May 2025
Latent Video Dataset Distillation Ning Li Antai Andy Liu Jingran Zhang Justin Cui DD VGen 104 0 0 23 Apr 2025
A Large-Scale Study on Video Action Dataset Condensation Yang Chen Sheng Guo Bo Zheng Limin Wang DD 133 2 0 13 Mar 2025
Graph Condensation: A Survey Xin Gao Junliang Yu Wei Jiang Tong Chen Wentao Zhang Hongzhi Yin DD 231 20 0 28 Jan 2025
Elucidating the Design Space of Dataset Condensation Shitong Shao Zikai Zhou Huanran Chen Zhiqiang Shen DD 106 9 0 20 Jan 2025
Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios Kai Wang Zekai Li Zhi-Qi Cheng Samir Khaki A. Sajedi Ramakrishna Vedantam Konstantinos N. Plataniotis Alexander G. Hauptmann Yang You DD 120 5 0 22 Oct 2024
Dataset Distillation via Knowledge Distillation: Towards Efficient Self-Supervised Pre-Training of Deep Networks S. Joshi Jiayi Ni Baharan Mirzasoleiman DD 145 2 0 03 Oct 2024
Distilling Long-tailed Datasets Zhenghao Zhao Haoxuan Wang Yuzhang Shang Kai Wang Yan Yan DD 89 3 0 24 Aug 2024
Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator Xin Zhang Jiawei Du Ping Liu Joey Tianyi Zhou DD 102 2 0 13 Aug 2024
A Label is Worth a Thousand Images in Dataset Distillation Tian Qin Zhiwei Deng David Alvarez-Melis DD 156 12 0 15 Jun 2024
Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition Xin Gao Tong Chen Wentao Zhang Junliang Yu Guanhua Ye Quoc Viet Hung Nguyen 106 7 0 22 May 2024
ATOM: Attention Mixer for Efficient Dataset Distillation Samir Khaki A. Sajedi Kai Wang Lucy Z. Liu Y. Lawryshyn Konstantinos N. Plataniotis 106 3 0 02 May 2024
Towards Adversarially Robust Dataset Distillation by Curvature Regularization Eric Xue Yijiang Li Haoyang Liu Yifan Shen Haohan Wang Haohan Wang DD 95 8 0 15 Mar 2024
Disentangled Condensation for Large-scale Graphs Zhenbang Xiao Shunyu Liu Yu Wang Tongya Zheng Mingli Song Mingli Song Tongya Zheng DD 197 6 0 18 Jan 2024
DataDAM: Efficient Dataset Distillation with Attention Matching A. Sajedi Samir Khaki Ehsan Amjadian Lucy Z. Liu Y. Lawryshyn Konstantinos N. Plataniotis DD 106 64 0 29 Sep 2023
Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching Tao Feng Jie Zhang Peizheng Wang Zhijie Wang Shengyuan Pang DD 108 0 0 29 May 2023
InstaHide: Instance-hiding Schemes for Private Distributed Learning Yangsibo Huang Zhao Song Keqin Li Sanjeev Arora FedML PICV 72 151 0 06 Oct 2020
Finite Versus Infinite Neural Networks: an Empirical Study Jaehoon Lee S. Schoenholz Jeffrey Pennington Ben Adlam Lechao Xiao Roman Novak Jascha Narain Sohl-Dickstein 63 213 0 31 Jul 2020
Flexible Dataset Distillation: Learn Labels Instead of Images Ondrej Bohdal Yongxin Yang Timothy M. Hospedales DD 78 110 0 15 Jun 2020
Dataset Condensation with Gradient Matching Bo Zhao Konda Reddy Mopuri Hakan Bilen DD 109 495 0 10 Jun 2020
Coresets via Bilevel Optimization for Continual Learning and Streaming Zalan Borsos Mojmír Mutný Andreas Krause CLL 73 232 0 06 Jun 2020
Neural Kernels Without Tangents Vaishaal Shankar Alex Fang Wenshuo Guo Sara Fridovich-Keil Ludwig Schmidt Jonathan Ragan-Kelley Benjamin Recht 44 90 0 04 Mar 2020
On the infinite width limit of neural networks with a standard parameterization Jascha Narain Sohl-Dickstein Roman Novak S. Schoenholz Jaehoon Lee 66 47 0 21 Jan 2020
Neural Tangents: Fast and Easy Infinite Neural Networks in Python Roman Novak Lechao Xiao Jiri Hron Jaehoon Lee Alexander A. Alemi Jascha Narain Sohl-Dickstein S. Schoenholz 64 228 0 05 Dec 2019
Optimizing Millions of Hyperparameters by Implicit Differentiation Jonathan Lorraine Paul Vicol David Duvenaud DD 104 414 0 06 Nov 2019
Soft-Label Dataset Distillation and Text Dataset Distillation Ilia Sucholutsky Matthias Schonlau DD 126 135 0 06 Oct 2019
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks Sanjeev Arora S. Du Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang Dingli Yu AAML 58 162 0 03 Oct 2019
Using Small Proxy Datasets to Accelerate Hyperparameter Search Sam Shleifer Eric Prokop DD 36 22 0 12 Jun 2019
On Exact Computation with an Infinitely Wide Neural Net Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang 193 922 0 26 Apr 2019
Meta-Learning with Differentiable Convex Optimization Kwonjoon Lee Subhransu Maji Avinash Ravichandran Stefano Soatto 91 1,267 0 07 Apr 2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation Trevor Hastie Andrea Montanari Saharon Rosset Robert Tibshirani 157 743 0 19 Mar 2019
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent Jaehoon Lee Lechao Xiao S. Schoenholz Yasaman Bahri Roman Novak Jascha Narain Sohl-Dickstein Jeffrey Pennington 180 1,097 0 18 Feb 2019
Dataset Distillation Tongzhou Wang Jun-Yan Zhu Antonio Torralba Alexei A. Efros DD 75 296 0 27 Nov 2018
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes Roman Novak Lechao Xiao Jaehoon Lee Yasaman Bahri Greg Yang Jiri Hron Daniel A. Abolafia Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 54 309 0 11 Oct 2018
Deep Convolutional Networks as shallow Gaussian Processes Adrià Garriga-Alonso C. Rasmussen Laurence Aitchison BDL UQCV 85 270 0 16 Aug 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 236 3,191 0 20 Jun 2018
Meta-learning with differentiable closed-form solvers Luca Bertinetto João F. Henriques Philip Torr Andrea Vedaldi ODL 80 928 0 21 May 2018
Gaussian Process Behaviour in Wide Deep Neural Networks A. G. Matthews Mark Rowland Jiri Hron Richard Turner Zoubin Ghahramani BDL 141 557 0 30 Apr 2018
Deep Neural Networks as Gaussian Processes Jaehoon Lee Yasaman Bahri Roman Novak S. Schoenholz Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 113 1,091 0 01 Nov 2017
Prototypical Networks for Few-shot Learning Jake C. Snell Kevin Swersky R. Zemel 269 8,114 0 15 Mar 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 169 2,878 0 14 Mar 2017
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 189 6,109 0 01 Jul 2016
Matching Networks for One Shot Learning Oriol Vinyals Charles Blundell Timothy Lillicrap Koray Kavukcuoglu Daan Wierstra VLM 335 7,313 0 13 Jun 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 310 7,971 0 23 May 2016
Gradient-based Hyperparameter Optimization through Reversible Learning D. Maclaurin David Duvenaud Ryan P. Adams DD 205 944 0 11 Feb 2015
Convolutional Kernel Networks Julien Mairal Piotr Koniusz Zaïd Harchaoui Cordelia Schmid 87 380 0 12 Jun 2014