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Parametric models, and particularly neural networks, require weight initialization as a starting point for gradient-based optimization.
Dataset2vec: Learning dataset meta-features
Jomaa, H. S., Grabocka, J., & Schmidt-Thieme, L. (2019) · 1905
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A simple sequentially rejective multiple test procedure
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Individual comparisons by ranking methods
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Statistical comparisons of classifiers over multiple data sets
Demšar, J. (2006) · 2006
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X., & Bengio, Y. (2010) · 2010
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Cross-domain sentiment classification via spectral feature alignment
Pan, S. J., Xiaochuan, N., Jian-Tao, S., Qiang, Y., & Zheng, C. (2010) · 2010
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A survey on transfer learning
Pan, S. J., & Yang, Q. (2010) · 2010
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Adam: A method for stochastic optimization
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015
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Optimization as a model for few-shot learning,
Ravi, S., & Larochelle, H. (2016) · 2016
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Meta-learning with memory-augmented neural networks
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Scalable hyperparameter optimization with products of gaussian process experts
Schilling, N., Wistuba, M., & Schmidt-Thieme, L. (2016) · 2016
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Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D. et al. (2016) · 2016
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Zagoruyko, S., & Komodakis, N. (2016) · 2016
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Openml benchmarking suites and the openml100
[Dataset] Bischl, B., Casalicchio, G., Feurer, M., Hutter, F., Lang, M., Mantovani, R. G., van Rijn, J. N., & Vanschoren, J. (2017) · 2017
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A simple neural attentive meta-learner
Mishra, N., Rohaninejad, M., Chen, X., & Abbeel, P. (2018) · 2018
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Concept learning through deep reinforcement learning with memory-augmented neural networks
Shi, J., Xu, J., Yao, Y., & Xu, B. (2019) · 2018
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Learning to compare: Relation network for few-shot learning
Sung, F., Yang, d. L., Yongxin an Zhang, Xiang, T., Torr, P. H., & Hospedales, T. M. (2018) · 2018
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Local deep-feature alignment for unsupervised dimension reduction
Zhang, J., Yu, J., & Tao, D. (2018) · 2018
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Deep learning for time series classification: a review
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019) · 2019
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Meta-learning with latent embedding optimization
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Emnist: Extending mnist to handwritten letters,
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One-shot imitation learning
Duan, Y., Andrychowicz, M., Stadie, B., Ho, O. J., Schneider, J., Sutskever, I., Abbeel, P., & Zaremba, W. (2017) · 2017
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Meta networks
Munkhdalai, T., & Yu, H. (2017) · 2017
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Prototypical networks for few-shot learning
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Probabilistic model-agnostic meta-learning
Finn, C., Xu, K., & Levine, S. (2018) · 2018
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., & Levine, S. (2017a)
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One-shot visual imitation learning via meta-learning
Finn, C., Yu, T., Zhang, T., Abbeel, P., & Levine, S. (2017b)
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Rusu, A. A., Rao, D., Sygnowski, J., Vinyals, O., Pascanu, R., Osindero, S., & Hadsell, R. (2019) · 2019
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Multi-label zero-shot human action recognition via joint latent ranking embedding
Wang, Q., & Chen, K. (2020) · 2019
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Named entity recognition in electronic health records using transfer learning bootstrapped neural networks
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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., & Larochelle, H. (2020) · 2020
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