Fetching the paper…
Reading the bibliography…
Dataset distillation has emerged as a strategy to overcome the hurdles associated with large datasets by learning a compact set of synthetic data that retains essential information from the original dataset.
On the kolmogorov-smirnov test for normality with mean and variance unknown
Lilliefors, H. W · 1967
Earlier work this paper cites.
Calculation of gauss quadrature rules
Golub, G. H. and Welsch, J. H · 1969
Earlier work this paper cites.
A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Hutchinson, M. F · 1989
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
Earlier work this paper cites.
A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Qualitatively characterizing neural network optimization problems
Goodfellow, I. J., Vinyals, O., and Saxe, A. M · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Tiny imagenet visual recognition challenge
Le, Y. and Yang, X · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Understanding black-box predictions via influence functions
Koh, P. W. and Liang, P · 2017
Earlier work this paper cites.
Critical learning periods in deep networks
Achille, A., Rovere, M., and Soatto, S · 2018
Earlier work this paper cites.
Visualizing the loss landscape of neural nets
Li, H., Xu, Z., Taylor, G., Studer, C., and Goldstein, T · 2018
Earlier work this paper cites.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2018
Cited alongside, same era.
Umap: Uniform manifold approximation and projection
McInnes, L., Healy, J., Saul, N., and Großberger, L · 2018
Cited alongside, same era.
Wang, T., Zhu, J.-Y., Torralba, A., and Efros, A. A · 2018
Cited alongside, same era.
An investigation into neural net optimization via hessian eigenvalue density
Ghorbani, B., Krishnan, S., and Xiao, Y · 2019
Cited alongside, same era.
Influence functions in deep learning are fragile
Basu, S., Pope, P., and Feizi, S · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
When less is more: Simplifying inputs aids neural network understanding
Schirrmeister, R. T., Liu, R., Hooker, S., and Ball, T · 2022
Later among the works it cites.
On implicit bias in overparameterized bilevel optimization
Vicol, P., Lorraine, J. P., Pedregosa, F., Duvenaud, D., and Grosse, R. B · 2022
Later among the works it cites.
Cafe: Learning to condense dataset by aligning features
Wang, K., Zhao, B., Peng, X., Zhu, Z., Yang, S., Wang, S., Huang, G., Bilen, H., Wang, X., and You, Y · 2022
Later among the works it cites.
Dataset distillation using neural feature regression
Zhou, Y., Nezhadarya, E., and Ba, J · 2022
Later among the works it cites.
Scaling up dataset distillation to imagenet-1k with constant memory
Cui, J., Wang, R., Si, S., and Hsieh, C.-J · 2023
Later among the works it cites.
Delving into effective gradient matching for dataset condensation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
Cited alongside, same era.
Dataset meta-learning from kernel ridge-regression
Nguyen, T., Chen, Z., and Lee, J · 2020
Cited alongside, same era.
Pyhessian: Neural networks through the lens of the hessian
Yao, Z., Gholami, A., Keutzer, K., and Mahoney, M. W · 2020
Cited alongside, same era.
Dataset distillation with infinitely wide convolutional networks
Nguyen, T., Novak, R., Xiao, L., and Lee, J · 2021
Cited alongside, same era.
Dataset condensation with differentiable siamese augmentation
Zhao, B. and Bilen, H · 2021
Cited alongside, same era.
Dataset condensation with gradient matching
Zhao, B., Mopuri, K. R., and Bilen, H · 2021
Cited alongside, same era.
Dataset distillation by matching training trajectories
Cazenavette, G., Wang, T., Torralba, A., Efros, A. A., and Zhu, J.-Y · 2022
Cited alongside, same era.
Jiang, Z., Gu, J., Liu, M., and Pan, D. Z · 2023
Later among the works it cites.
On the size and approximation error of distilled sets
Maalouf, A., Tukan, M., Loo, N., Hasani, R., Lechner, M., and Rus, D · 2023
Later among the works it cites.
Sachdeva, N. and McAuley, J · 2023
Later among the works it cites.
Error discovery by clustering influence embeddings
Wang, F., Adebayo, J., Tan, S., Garcia-Olano, D., and Kokhlikyan, N · 2023
Later among the works it cites.
Multimodal dataset distillation for image-text retrieval
Wu, X., Deng, Z., and Russakovsky, O · 2023
Later among the works it cites.
Dataset condensation with distribution matching
Zhao, B. and Bilen, H · 2023
Later among the works it cites.
Improved distribution matching for dataset condensation
Zhao, G., Li, G., Qin, Y., and Yu, Y · 2023
Later among the works it cites.
Towards mitigating architecture overfitting in dataset distillation
Zhong, X. and Liu, C · 2023
Later among the works it cites.
Frequency domain-based dataset distillation
Shin, D., Shin, S., and Moon, I.-C · 2024
Closest in time.