Fetching the paper…
Reading the bibliography…
The great success of machine learning with massive amounts of data comes at a price of huge computation costs and storage for training and tuning.
Statistics of natural images and models
Huang, J. and Mumford, D · 1999
Earlier work this paper cites.
Pattern recognition and machine learning
Bishop, C. M · 2006
Earlier work this paper cites.
The EM algorithm and extensions , volume 382
McLachlan, G. J. and Krishnan, T · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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.
Online dictionary learning for sparse coding
Mairal, J., Bach, F., Ponce, J., and Sapiro, G · 2009
Earlier work this paper cites.
Herding dynamical weights to learn
Welling, M · 2009
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
Earlier work this paper cites.
Gradient-based hyperparameter optimization through reversible learning
Maclaurin, D., Duvenaud, D., and Adams, R · 2015
Earlier work this paper cites.
Accelerating the super-resolution convolutional neural network
Dong, C., Loy, C. C., and Tang, X · 2016
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.
Phillips, J. M · 2016
Earlier work this paper cites.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Earlier work this paper cites.
Learning without forgetting
Li, Z. and Hoiem, D · 2017
Cited alongside, same era.
icarl: Incremental classifier and representation learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., and Lampert, C. H · 2017
Cited alongside, same era.
Towards end-to-end speech recognition with deep convolutional neural networks
Zhang, Y., Pezeshki, M., Brakel, P., Zhang, S., Bengio, C. L. Y., and Courville, A · 2017
Cited alongside, same era.
Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2017
Cited alongside, same era.
Wang, T., Zhu, J.-Y., Torralba, A., and Efros, A. A · 2018
Cited alongside, same era.
Speech commands: A dataset for limited-vocabulary speech recognition
Puzzle mix: Exploiting saliency and local statistics for optimal mixup
Kim, J.-H., Choo, W., and Song, H. O · 2020
Later among the works it cites.
Gdumb: A simple approach that questions our progress in continual learning
Prabhu, A., Torr, P. H., and Dokania, P. K · 2020
Later among the works it cites.
Implicit neural representations with periodic activation functions
Sitzmann, V., Martel, J. N., Bergman, A. W., Lindell, D. B., and Wetzstein, G · 2020
Later among the works it cites.
Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data
Such, F. P., Rawal, A., Lehman, J., Stanley, K., and Clune, J · 2020
Later among the works it cites.
Contrastive multiview coding
Tian, Y., Krishnan, D., and Isola, P · 2020
Later among the works it cites.
Rainbow memory: Continual learning with a memory of diverse samples
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Warden, P · 2018
Cited alongside, same era.
Autoaugment: Learning augmentation strategies from data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 2019
Cited alongside, same era.
On the relation between the sharpest directions of dnn loss and the sgd step length
Jastrzebski, S., Kenton, Z., Ballas, N., Fischer, A., Bengio, Y., and Storkey, A · 2019
Cited alongside, same era.
Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., and Wermter, S · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q · 2019
Cited alongside, same era.
An empirical study of example forgetting during deep neural network learning
Toneva, M., Sordoni, A., Combes, R. T. d., Trischler, A., Bengio, Y., and Gordon, G. J · 2019
Cited alongside, same era.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., and Yoo, Y · 2019
Cited alongside, same era.
Bang, J., Kim, H., Yoo, Y., Ha, J.-W., and Choi, J · 2021
Later among the works it cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2021
Later among the works it cites.
Co-mixup: Saliency guided joint mixup with supermodular diversity
Kim, J.-H., Choo, W., Jeong, H., and Song, H. O · 2021
Later among the works it cites.
Dataset distillation with infinitely wide convolutional networks
Nguyen, T., Novak, R., Xiao, L., and Lee, J · 2021
Later among the works it cites.
Carbon emissions and large neural network training
Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., and Dean, J · 2021
Later among the works it cites.
Soft-label dataset distillation and text dataset distillation
Sucholutsky, I. and Schonlau, M · 2021
Later among the works it cites.
Dataset condensation with gradient matching
Zhao, B., Mopuri, K. R., and Bilen, H · 2021
Later among the works it cites.
Dataset distillation by matching training trajectories
Cazenavette, G., Wang, T., Torralba, A., Efros, A. A., and Zhu, J.-Y · 2022
Closest in time.