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Mar 26, 2024 · In this paper, an efficient non-negative least mean squares algorithm based on q-gradient is developed. At first, the q-gradient of the mean square error cost ...
Feb 19, 2024 · The REINFORCE algorithm is a Monte Carlo policy gradient method. It collects episodes using the policy, estimates the gradient from that episode, and updates ...
Jun 16, 2023 · We introduce a quality measure that enables us to optimize the classical post-processing required for action selec- tion, inspired by local and global quantum ...
Mar 18, 2024 · Gradient orientation and gradient magnitude can segment an image by identifying regions with similar gradient properties. For example, regions with similar ...
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Dec 18, 2023 · An electrochemical gradient is a gradient of electrochemical potential, usually for an ion that can move across a membrane. The gradient consists of two ...
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Aug 29, 2023 · Gradient Clipping is a method where the error derivative is changed or clipped to a threshold during backward propagation through the network, and using the ...
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Feb 5, 2024 · A Polak-Ribière-Polyak (PRP) algorithm is one of the oldest and popular conjugate gradient algorithms for solving nonlinear unconstrained optimization ...
Dec 14, 2023 · The Policy Gradient Theorem provides an analytic expression for the gradient of an objective (such as return) with respect to the policy parameter. Let's denote ...
Mar 23, 2024 · In this tutorial, you will implement two reinforcement learning algorithms based on parametrized/variational quantum circuits (PQCs or VQCs), namely a policy- ...
