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Neural architecture search (NAS) has become an important approach to automatically find effective architectures.
Curriculum learning
Bengio, Y., Louradour, J., Collobert, R., and Weston, J · 2009
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
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Self-paced learning for latent variable models
Kumar, M. P., Packer, B., and Koller, D · 2010
Earlier work this paper cites.
How do humans teach: On curriculum learning and teaching dimension
Khan, F., Mutlu, B., and Zhu, J · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P · 2013
Earlier work this paper cites.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
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A neural autoregressive approach to attention-based recognition
Zheng, Y., Zemel, R. S., Zhang, Y.-J., and Larochelle, H · 2015
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Knowledge matters: Importance of prior information for optimization
Gülçehre, Ç. and Bengio, Y · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
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Deep pyramidal residual networks
Han, D., Kim, J., and Kim, J · 2017
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Cited alongside, same era.
Variational deep embedding: An unsupervised and generative approach to clustering
Jiang, Z., Zheng, Y., Tan, H., Tang, B., and Zhou, H · 2017
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., and Darrell, T · 2017
Cited alongside, same era.
Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2017
Cited alongside, same era.
Adversarial learning with local coordinate coding
Cao, J., Guo, Y., Wu, Q., Shen, C., Huang, J., and Tan, M · 2018
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L · 2018
Ccnet: Criss-cross attention for semantic segmentation
Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., and Liu, W · 2019
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DARTS: Differentiable Architecture Search
Liu, H., Simonyan, K., and Yang, Y · 2019
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Teacher-student curriculum learning
Matiisen, T., Oliver, A., Cohen, T., and Schulman, J · 2019
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Regularized evolution for image classifier architecture search
Real, E., Aggarwal, A., Huang, Y., and Le, Q. V · 2019
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Mnasnet: Platform-aware neural architecture search for mobile
Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q. V · 2019
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Dynamic curriculum learning for imbalanced data classification
Wang, Y., Gan, W., Yang, J., Wu, W., and Yan, J · 2019
Later among the works it cites.
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Cited alongside, same era.
Instanas: Instance-aware neural architecture search
Cheng, A.-C., Lin, C. H., Juan, D.-C., Wei, W., and Sun, M · 2018
Cited alongside, same era.
Neural architecture optimization
Luo, R., Tian, F., Qin, T., Chen, E., and Liu, T.-Y · 2018
Cited alongside, same era.
Efficient neural architecture search via parameter sharing
Pham, H., Guan, M. Y., Zoph, B., Le, Q. V., and Dean, J · 2018
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V · 2018
Cited alongside, same era.
Proxylessnas: Direct neural architecture search on target task and hardware
Cai, H., Zhu, L., and Han, S · 2019
Cited alongside, same era.
A generalized locally linear factorization machine with supervised variational encoding
Chen, X., Zheng, Y., Zhao, P., Jiang, Z., Ma, W., and Huang, J · 2019
Cited alongside, same era.
SNAS: stochastic neural architecture search
Xie, S., Zheng, H., Liu, C., and Lin, L · 2019
Later among the works it cites.
Cars: Continuous evolution for efficient neural architecture search
Yang, Z., Wang, Y., Chen, X., Shi, B., Xu, C., Xu, C., Tian, Q., and Xu, C · 2019
Later among the works it cites.
Graph convolutional networks for temporal action localization
Zeng, R., Huang, W., Tan, M., Rong, Y., Zhao, P., Huang, J., and Gan, C · 2019
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Leveraging prior-knowledge for weakly supervised object detection under a collaborative self-paced curriculum learning framework
Zhang, D., Han, J., Zhao, L., and Meng, D · 2019
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Deep-aligned convolutional neural network for skeleton-based action recognition and segmentation
Hosseini, B., Montagne, R., and Hammer, B · 2020
Closest in time.
Towards fast adaptation of neural architectures with meta learning
Lian, D., Zheng, Y., Xu, Y., Lu, Y., Lin, L., Zhao, P., Huang, J., and Gao, S · 2020
Closest in time.
Discrimination-aware network pruning for deep model compression
Liu, J., Zhuang, B., Zhuang, Z., Guo, Y., Huang, J., Zhu, J., and Tan, M · 2020
Closest in time.