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context [2018/11/24 12:13]
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context [2019/01/16 17:11] (current)
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 https://​arxiv.org/​pdf/​1703.06408v1.pdf Multilevel Context Representation for Improving Object Recognition https://​arxiv.org/​pdf/​1703.06408v1.pdf Multilevel Context Representation for Improving Object Recognition
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 This paper This paper
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 https://​www.nature.com/​articles/​s41467-018-06781-2 Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences https://​www.nature.com/​articles/​s41467-018-06781-2 Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences
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 +https://​arxiv.org/​abs/​1901.03415v1 Context Aware Machine Learning
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 +The embedding of an observation can also be decomposed into a weighted sum of two vectors, representing its context-free and context-sensitive parts
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 + new architecture for modeling attention in deep neural networks. More surprisingly,​ our new principle provides a novel understanding of the gates and equations defined by the long short term memory model, which also leads to a new model that is able to converge significantly faster and achieve much lower prediction errors. Furthermore,​ our principle also inspires a new type of generic neural network layer that better resembles real biological neurons than the traditional linear mapping plus nonlinear activation based architecture. Its multi-layer extension provides a new principle for deep neural networks which subsumes residual network (ResNet) as its special case, and its extension to convolutional neutral network model accounts for irrelevant input (e.g., background in an image) in addition to filtering.
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