We introduce a learning algorithm for unsupervised neural networks based on ideas from statistical mechanics. The algorithm is derived from a mean field ...
We propose a learning algorithm that looks for synaptic strengths that maximize the network's performance. Patterns with lower stabilities are more effective in ...
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Feb 1, 1999 · A mean field learning algorithm for unsupervised neural networks. Authors: Lawrence Saul. View Profile. ,. Michael Jordan.
In this work, we define and apply a mean field theory (MFT) approximation to the statistical mechanics system that is defined hy the BM algorithm. The.
Missing: Unsupervised | Show results with:Unsupervised
Abstract. This paper introduces a new method based on Deep Galerkin Methods (DGMs) for solving high-dimensional stochastic Mean Field Games (MFGs). We.
A mean field theory learning algorithm for neural networks. Complex Systems, !,. 995-1019. Peterson, C., & Anderson, J. R. (1988). Neural networks and. NP ...
Dec 5, 2017 · Our algorithm provides a series of mean- eld observables revealing how the learning proceeds [3]. Finally, a particularly exciting application ...
Abstract. The mean field algorithm is a widely used approximate inference algorithm for graphical models whose exact inference is intractable. In.
Missing: Unsupervised | Show results with:Unsupervised
May 13, 2020 · We review a selection of classical mean-field methods and recent progress relevant for inference in neural networks. In particular, we remind ...
Jan 18, 2019 · Recurrent neural networks have been extensively studied in the context of neuroscience and machine learning.