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A recent trend in compressed sensing is to consider nonconvex optimization techniques for sparse recovery. The important case of $F$ -minimization has become of particular interest, for which the exact reconstruction condition (ERC) in the noiseless setting can be precisely characterized by the null space property (NSP). However, little work has been done concerning its robust reconstruction condition...
Recent work in compressed sensing has shown the possibility reducing the number of measurements via non-convex optimization methods. Most of these methods can be studied in the general framework called “F-minimization”, for which the relation between the noiseless exact recovery condition (ERC) and noisy robust recovery condition (RRC) was not fully understood. In this paper, we associate each set...
Task decomposition is an effective approach to accelerate reinforcement learning, but how agent can discover autonomously sub-goals remains an open problem. We propose an approach to make agent can take advantage of state trajectories it learned form training episodes to arrange states in different layers according to the shortest distance of them from the goal state, then to reach these state layers...
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