Proximal Deep Structured Models

NeurIPS 2016 Shenlong WangSanja FidlerRaquel Urtasun

Many problems in real-world applications involve predicting continuous-valued random variables that are statistically related. In this paper, we propose a powerful deep structured model that is able to learn complex non-linear functions which encode the dependencies between continuous output variables... (read more)

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