WebMar 11, 2024 · Training deep neural networks with stochastic gradient descent (SGD) can often achieve zero training loss on real-world tasks although the optimization landscape is known to be highly non-convex. To understand the success of SGD for training deep neural networks, this work presents a mean-field analysis of deep residual networks, based on a … WebThis book covers the basic theory of mean field game (MFG) and its applications in wireless networks. It starts with an overview of the current and future state-of-the-art in 5G and 6G wireless networks.
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Mean Field Game and its Applications in Wireless Networks
WebThe mean field theory considers the interactions within the population of agents are approximated by those between a single agent and the average effect of its neighbouring agents, which will effectively reduce the “nonstationary” problem in MARL system. ... Convergence analysis. In previous works, the proof of mean field Q-learning ... WebMay 2, 2024 · Mean Field Analysis of Neural Networks: A Law of Large Numbers. Machine learning, and in particular neural network models, have revolutionized fields such as … WebApr 1, 2002 · Abstract. We develop mean-field approaches for probabilistic independent component analysis (ICA). The sources are estimated from the mean of their posterior distribution and the mixing matrix (and noise level) is estimated by maximum a posteriori (MAP). The latter requires the computation of (a good approximation to) the correlations … in worse health