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Many real-world situations allow for the acquisition of additional relevant information when making an assessment with limited or uncertain data.
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Gu, S., Lillicrap, T., Sutskever, I., and Levine, S · 2016
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Brabec, J. and Machlica, L · 2018
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Cai, J., Luo, J., Wang, S., and Yang, S · 2018
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Reinforcement learning of active vision for manipulating objects under occlusions
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He, H., Mineiro, P., and Karampatziakis, N · 2016
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A survey on feature selection
Miao, J. and Niu, L · 2016
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The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
Bagnall, A., Lines, J., Bostrom, A., Large, J., and Keogh, E · 2017
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Dua, D. and Graff, C · 2017
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Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., and Liu, H · 2017
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Consistent generative query networks
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Eddi: Efficient dynamic discovery of high-value information with partial vae
Ma, C., Tschiatschek, S., Palla, K., Hernández-Lobato, J. M., Nowozin, S., and Zhang, C · 2018
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Temporal difference models: Model-free deep rl for model-based control
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Joint active feature acquisition and classification with variable-size set encoding
Shim, H., Hwang, S. J., and Yang, E · 2018
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Set transformer: A framework for attention-based permutation-invariant neural networks
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Yoo, D. and Kweon, I. S · 2019
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Odin: Optimal discovery of high-value information using model-based deep reinforcement learning
Zannone, S., Hernandez Lobato, J. M., Zhang, C., and Palla, K · 2019
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