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I think that the tasks you are referring to are the Simultaneous localization and mapping (SLAM) and, in particular, the Structure from Motion (SfM). These methods are usually based on geometrical constraints and do not employ neural networks, but there exist some recent methods that make use of CNNs (such as this one). Structure from Motion algorithms are ...


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I would recommend doing is allowing your network to output any real number and then clipping the output. For instance, I was working with an agent that had to learn an angle between $[0, 2\pi]$ and $[0, 1]$. If the network outputted e.g. 10 in the first dimension then this would just be clipped to $2\pi$. This way the agent only learns about actions within ...


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A kernel function $f : \mathcal{X} \times \mathcal{X} \rightarrow \mathbb{R}$ is a valid support vector kernel if it is a Mercer kernel. Mercer's condition essentially ensures that the Gram matrix of the kernel is positive semi-definite. Interestingly, this ensures that the SVM objective is convex. The Euclidean distance function does not satisfy Mercer's ...


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