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Modern meta-learning approaches for image classification rely on increasingly deep networks to achieve state-of-the-art performance, making batch normalization an essential component of meta-learning pipelines.
Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
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Empirical bayes for learning to learn
Heskes, T · 2000
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Task clustering and gating for Bayesian multitask learning
Bakker, B. and Heskes, T · 2003
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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MNIST handwritten digit database
LeCun, Y., Cortes, C., and Burges, C · 2010
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One shot learning of simple visual concepts
Lake, B., Salakhutdinov, R., Gross, J., and Tenenbaum, J · 2011
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Learning to learn
Thrun, S. and Pratt, L · 2012
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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Hypernetworks
Ha, D., Dai, A., and Le, Q. V · 2016
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Salimans, T. and Kingma, D. P · 2016
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Instance normalization: The missing ingredient for fast stylization
Ulyanov, D., Vedaldi, A., and Lempitsky, V · 2016
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Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., kavukcuoglu, k., and Wierstra, D · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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Code for "Model-agnostic meta-learning for fast adaptation of deep networks"
Finn, C. B · 2017
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Batch renormalization: Towards reducing minibatch dependence in batch-normalized models
Ioffe, S · 2017
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Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H · 2017
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Code for the nips 2017 paper "prototypical networks for few-shot learning"
Snell, J · 2017
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Meta-learning for semi-supervised few-shot classification
Ren, M., Ravi, S., Triantafillou, E., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., and Zemel, R. S · 2018
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Group normalization
Wu, Y. and He, K · 2018
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Meta-learning probabilistic inference for prediction
Gordon, J., Bronskill, J., Bauer, M., Nowozin, S., and Turner, R · 2019
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Reconciling meta-learning and continual learning with online mixtures of tasks
Jerfel, G., Grant, E., Griffiths, T., and Heller, K. A · 2019
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Learning to propagate labels: transductive propagation network for few-shot learning
Liu, Y., Lee, J., Park, M., Kim, S., Yang, E., Hwang, S., and Yang, Y · 2019
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Deep online learning via meta-learning: Continual adaptation for model-based RL
Nagabandi, A., Finn, C., and Levine, S · 2019
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Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
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A re-implementation of "prototypical networks for few-shot learning"
Chen, Y · 2018
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Recasting gradient-based meta-learning as hierarchical bayes
Grant, E., Finn, C., Levine, S., Darrell, T., and Griffiths, T · 2018
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Cosine normalization: Using cosine similarity instead of dot product in neural networks
Luo, C., Zhan, J., Xue, X., Wang, L., Ren, R., and Yang, Q · 2018
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A simple neural attentive meta-learner
Mishra, N., Rohaninejad, M., Chen, X., and Abbeel, P · 2018
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Batch-instance normalization for adaptively style-invariant neural networks
Nam, H. and Kim, H.-E · 2018
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Meta-learning with implicit gradients
Rajeswaran, A., Finn, C., Kakade, S. M., and Levine, S · 2019
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Singh, S. and Krishnan, S · 2019
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Code for "Meta-dataset: A dataset of datasets for learning to learn from few examples"
Triantafillou, E., Zhu, T., Dumoulin, V., Lamblin, P., Xu, K., Goroshin, R., Gelada, C., Swersky, K., Manzagol, P.-A., and Larochelle, H · 2019
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Meta-learning in neural networks: A survey
Hospedales, T., Antoniou, A., Micaelli, P., and Storkey, A · 2020
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Meta-dataset: A dataset of datasets for learning to learn from few examples
Triantafillou, E., Zhu, T., Dumoulin, V., Lamblin, P., Evci, U., Xu, K., Goroshin, R., Gelada, C., Swersky, K., Manzagol, P.-A., and Larochelle, H · 2020
Closest in time.