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Numerical tests show that the learned models outperform fixed transforms and per-image optimization while matching the performance of much larger unitary architectures, yet with far fewer parameters.
This work shows that the gradient of the instantaneous negative log-likelihood of a warped GP admits an exact recursive computation, and proposes a novel online method that jointly updates the latent GP moments and optimizes the warping parameters.
This paper develops an online, off-policy policy-iteration framework for reinforcement learning (RL), based on sparse Gaussian-mixture-model Q-functions (S-GMM-QFs), enabling interpretable sparsification through smooth regularization that facilitates Riemannian-based optimization.
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