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The standard approaches to neural network implementation yield powerful function approximation capabilities but are limited in their abilities to learn meta representations and reason probabilistic uncertainties in their predictions.
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M. Huisman, J. N. van Rijn, and A. Plaat, Metalearning for Deep Neural Networks . Cham: Springer International Publishing, 2022, pp. 237–267. [Online]. Available: https://doi.org/10.1007/978-3-030-67024-5_13
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M. Kim, K. R. Go, and S.-Y. Yun, “Neural processes with stochastic attention: Paying more attention to the context dataset,” in ICLR , 2022. [Online]. Available: https://openreview.net/forum?id=JPkQwEdYn8
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2022
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S. Markou, J. Requeima, W. Bruinsma, A. Vaughan, and R. E. Turner, “Practical conditional neural process via tractable dependent predictions,” in ICLR , 2022. [Online]. Available: https://openreview.net/forum?id=3pugbNqOh5m
2022
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D. Kim, S. Cho, W. Lee, and S. Hong, “Multi-task processes,” in ICLR , 2022. [Online]. Available: https://openreview.net/forum?id=9otKVlgrpZG
2022
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Z. Ye and L. Yao, “Contrastive conditional neural processes,” in CVPR , June 2022
2022
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N. Gao, H. Ziesche, N. A. Vien, M. Volpp, and G. Neumann, “What matters for meta-learning vision regression tasks?” in CVPR , 2022, pp. 14 776–14 786
2022
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N. McGreivy and A. Hakim, “Convolutional layers are not translation equivariant,” 2022
2022
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J. Shen, X. Zhen, M. Worring, and L. Shao, “Multi-task neural processes,” 2022. [Online]. Available: https://openreview.net/forum?id=wfRZkDvxOqj
2022
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G. Wang, X. Xu, T. Zhong, and F. Zhou, “Conditional collaborative filtering process for top-k recommender system (student abstract),” AAAI , 2022
2022
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I. Čvorović-Hajdinjak, A. B. Kovačević, D. Ilić, L. Č. Popović, X. Dai, I. Jankov, V. Radović, P. Sánchez-Sáez, and R. Nikutta, “Conditional neural process for nonparametric modeling of active galactic nuclei light curves,” Astronomische Nachrichten , vol. 343, no. 1-2, p. e210103, 2022
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2022
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Y. Wang, K. Wang, W. Cai, and X. Yue, “Np-ode: Neural process aided ordinary differential equations for uncertainty quantification of finite element analysis,” IISE Transactions , vol. 54, no. 3, pp. 211–226, 2022
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2022
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D. M. Wu, M. Chinazzi, A. Vespignani, Y.-A. Ma, and R. Yu, “Multi-fidelity hierarchical neural processes,” 2022
2022
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J. Wang, T. Lukasiewicz, D. Massiceti, X. Hu, V. Pavlovic, and A. Neophytou, “Np-match: When neural processes meet semi-supervised learning,” in ICML . PMLR, 2022, pp. 22 919–22 934
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2022
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T. Lukasiewicz and J. Wang, “Np- match: When neural processes meet semi- supervised learning,” in ICML . PMLR, 2022
2022
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W. Bruinsma, S. Markou, J. Requeima, A. Y. K. Foong, A. Vaughan, T. Andersson, A. Buonomo, S. Hosking, and R. E. Turner, “Autoregressive conditional neural processes,” in ICLR , 2023. [Online]. Available: https://openreview.net/forum?id=OAsXFPBfTBh
2023
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Q. Wang, M. Federici, and H. van Hoof, “Bridge the inference gaps of neural processes via expectation maximization,” in ICLR , 2023. [Online]. Available: https://openreview.net/forum?id=A7v2DqLjZdq
2023
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S. Jha, D. Gong, H. Zhao, and L. Yao, “NPCL: Neural processes for uncertainty-aware continual learning,” in NeurIPS , 2023. [Online]. Available: https://openreview.net/forum?id=huh0XmSdBK
2023
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J. S. Shen, X. Zhen, Q. Wang, and M. Worring, “Episodic multi-task learning with heterogeneous neural processes,” in NeurIPS , 2023. [Online]. Available: https://openreview.net/forum?id=FXU4aR2uif
2023
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