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Neural Processes (NPs) are popular meta-learning methods for efficiently modelling predictive uncertainty.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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On the properties of neural machine translation: Encoder-decoder approaches
Cho, K., Van Merriënboer, B., Bahdanau, D., and Bengio, Y · 2014
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y · 2014
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Emnist: Extending mnist to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and Van Schaik, A · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
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A simple neural attentive meta-learner
Mishra, N., Rohaninejad, M., Chen, X., and Abbeel, P · 2018
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Riquelme, C., Tucker, G., and Snoek, J · 2018
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Convolutional conditional neural processes
Gordon, J., Bruinsma, W. P., Foong, A. Y., Requeima, J., Dubois, Y., and Turner, R. E · 2019
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Attentive neural processes
Kim, H., Mnih, A., Schwarz, J., Garnelo, M., Eslami, A., Rosenbaum, D., Vinyals, O., and Teh, Y. W · 2019
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Predicting dynamic embedding trajectory in temporal interaction networks
Kumar, S., Zhang, X., and Leskovec, J · 2019
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Set transformer: A framework for attention-based permutation-invariant neural networks
Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S., and Teh, Y. W · 2019
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Fast and flexible multi-task classification using conditional neural adaptive processes
Requeima, J., Gordon, J., Bronskill, J., Nowozin, S., and Turner, R. E · 2019
Cited alongside, same era.
Sequential neural processes
Singh, G., Yoon, J., Son, Y., and Ahn, S · 2019
Cited alongside, same era.
Willi, T., Masci, J., Schmidhuber, J., and Osendorfer, C · 2019
Cited alongside, same era.
Bootstrapping neural processes
Lee, J., Lee, Y., Kim, J., Yang, E., Hwang, S. J., and Teh, Y. W · 2020
Cited alongside, same era.
Intensity-free learning of temporal point processes
Shchur, O., Biloš, M., and Günnemann, S · 2020
Cited alongside, same era.
Transformer hawkes process
Zuo, S., Jiang, H., Li, Z., Zhao, T., and Zha, H · 2020
Cited alongside, same era.
The neural process family: Survey, applications and perspectives
Jha, S., Gong, D., Wang, X., Turner, R. E., and Yao, L · 2022
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Transformers in vision: A survey
Khan, S., Naseer, M., Hayat, M., Zamir, S. W., Khan, F. S., and Shah, M · 2022
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Memory-efficient gaussian fitting for depth images in real time
Li, P. Z. X., Karaman, S., and Sze, V · 2022
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How neural processes improve graph link prediction
Liang, H. and Gao, J · 2022
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A survey of transformers
Lin, T., Wang, Y., Liu, X., and Qiu, X · 2022
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Transformer neural processes: Uncertainty-aware meta learning via sequence modeling
Nguyen, T. and Grover, A · 2022
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Equivariant learning of stochastic fields: Gaussian processes and steerable conditional neural processes
Holderrieth, P., Hutchinson, M. J., and Teh, Y. W · 2021
Cited alongside, same era.
Task-adaptive neural process for user cold-start recommendation
Lin, X., Wu, J., Zhou, C., Pan, S., Cao, Y., and Wang, B · 2021
Cited alongside, same era.
Convolutional conditional neural processes for local climate downscaling
Vaughan, A., Tebbutt, W., Hosking, J. S., and Turner, R. E · 2021
Cited alongside, same era.
Varibad: Variational bayes-adaptive deep rl via meta-learning
Zintgraf, L., Schulze, S., Lu, C., Feng, L., Igl, M., Shiarlis, K., Gal, Y., Hofmann, K., and Whiteson, S · 2021
Cited alongside, same era.
Meta-learning regrasping strategies for physical-agnostic objects
Chen, R., Gao, N., Vien, N. A., Ziesche, H., and Neumann, G · 2022
Cited alongside, same era.
Transformers as meta-learners for implicit neural representations
Chen, Y. and Wang, X · 2022
Cited alongside, same era.
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Self-attention does not need o ( n 2 ) o(n^{2}) memory
Rabe, M. N. and Staats, C · 2022
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How to certify machine learning based safety-critical systems? a systematic literature review
Tambon, F., Laberge, G., An, L., Nikanjam, A., Mindom, P. S. N., Pequignot, Y., Khomh, F., Antoniol, G., Merlo, E., and Laviolette, F · 2022
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Memformer: A memory-augmented transformer for sequence modeling
Wu, Q., Lan, Z., Qian, K., Gu, J., Geramifard, A., and Yu, Z · 2022
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Meta temporal point processes
Bae, W., Ahmed, M. O., Tung, F., and Oliveira, G. L · 2023
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Autoregressive conditional neural processes
Bruinsma, W., Markou, S., Requeima, J., Foong, A. Y. K., Andersson, T., Vaughan, A., Buonomo, A., Hosking, S., and Turner, R. E · 2023
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Latent bottlenecked attentive neural processes
Feng, L., Hajimirsadeghi, H., Bengio, Y., and Ahmed, M. O · 2023
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RWKV: Reinventing RNNs for the transformer era
Peng, B., Alcaide, E., Anthony, Q. G., Albalak, A., Arcadinho, S., Biderman, S., Cao, H., Cheng, X., Chung, M. N., Derczynski, L., Du, X., Grella, M., GV, K. K., He, X., Hou, H., Kazienko, P., Kocon, J., Kong, J., Koptyra, B., Lau, H., Lin, J., Mantri, K. S. I., Mom, F., Saito, A., Song, G., Tang, X., Wind, J. S., Woźniak, S., Zhang, Z., Zhou, Q., Zhu, J., and Zhu, R.-J · 2023
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