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Does deep learning assume that the fitness landscape on which the gradient descent occurs is a smooth one? One can interpret this question from a formal-mathematical standpoint and from a more "intuitively-practical" standpoint. From the formal point of view, smoothness is the requirement that the function is continuous with continuous first ...


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Main answer To answer your question as directly as possible: No, deep learning does not make that "assumption". But you're close. Just swap the word "assumption" with "imposition". Deep learning sets things up such that the landscape is (mostly) smooth and always continuous*, and therefore it is possible to do some sort of ...


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I'm going to take the fitness landscape to be the graph of the loss function, $\mathcal{G} = \{\left(\theta, L(\theta)\right) : \theta \in \mathbb{R}^n\}$, where $\theta$ parameterises the network (i.e. it is the weights and biases) and $L$ is a given loss function; in other words, the surface you would get by plotting the loss function against its ...


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In order to have anything resembling reinforcement learning you must at the very least have a set of states $S$ and a set of actions $A$. In your formulation I can vaguely identify the set of states $S$ as all possible $(x,y,z)$ triplets. But don't see anything in your description that could be interpreted as a set of actions $A$. You either oversimplified ...


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