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Found 9 papers, 0 papers with code
A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification
Motivated by the recent application of approximate message passing (AMP) to the analysis of convex optimizations in multi-class classifications [Loureiro, et.
Analysis of Random Sequential Message Passing Algorithms for Approximate Inference
We analyze the dynamics of a random sequential message passing algorithm for approximate inference with large Gaussian latent variable models in a student-teacher scenario.
Exact solution to the random sequential dynamics of a message passing algorithm
We analyze the random sequential dynamics of a message passing algorithm for Ising models with random interactions in the large system limit.
A Dynamical Mean-Field Theory for Learning in Restricted Boltzmann Machines
We define a message-passing algorithm for computing magnetizations in Restricted Boltzmann machines, which are Ising models on bipartite graphs introduced as neural network models for probability distributions over spin configurations.
Understanding the dynamics of message passing algorithms: a free probability heuristics
We use freeness assumptions of random matrix theory to analyze the dynamical behavior of inference algorithms for probabilistic models with dense coupling matrices in the limit of large systems.
Analysis of Bayesian Inference Algorithms by the Dynamical Functional Approach
We analyze the dynamics of an algorithm for approximate inference with large Gaussian latent variable models in a student-teacher scenario.
Memory-free dynamics for the TAP equations of Ising models with arbitrary rotation invariant ensembles of random coupling matrices
We propose an iterative algorithm for solving the Thouless-Anderson-Palmer (TAP) equations of Ising models with arbitrary rotation invariant (random) coupling matrices.
Expectation Propagation for Approximate Inference: Free Probability Framework
We study asymptotic properties of expectation propagation (EP) -- a method for approximate inference originally developed in the field of machine learning.
Self-Averaging Expectation Propagation
Our approach extends the framework of (generalized) approximate message passing -- assumes zero-mean iid entries of the measurement matrix -- to a general class of random matrix ensembles.
