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Found 9 papers, 5 papers with code
On Dominant Interference in Random Networks and Communication Reliability
In this paper, we study the characteristics of dominant interference power with directional reception in a random network modelled by a Poisson Point Process.
Information Theory Signal Processing Information Theory
Powering Hidden Markov Model by Neural Network based Generative Models
In the proposed GenHMM, each HMM hidden state is associated with a neural network based generative model that has tractability of exact likelihood and provides efficient likelihood computation.
Neural Network based Explicit Mixture Models and Expectation-maximization based Learning
A single latent variable is used as the common input to all the neural networks.
$α$ Belief Propagation for Approximate Inference
To gain a better understanding of BP in general graphs, we derive an interpretable belief propagation algorithm that is motivated by minimization of a localized $\alpha$-divergence.
Locally Convex Sparse Learning over Networks
Our expectation is that local estimates in each node improve fast and converge, resulting in a limited demand for communication of estimates between nodes and reducing the processing time.
Estimate Exchange over Network is Good for Distributed Hard Thresholding Pursuit
The algorithm is iterative and exchanges intermediate estimates of a sparse signal over a network.
Entropy-regularized Optimal Transport Generative Models
We investigate the use of entropy-regularized optimal transport (EOT) cost in developing generative models to learn implicit distributions.
$α$ Belief Propagation as Fully Factorized Approximation
Belief propagation (BP) can do exact inference in loop-free graphs, but its performance could be poor in graphs with loops, and the understanding of its solution is limited.
Region-based Energy Neural Network for Approximate Inference
We term our model Region-based Energy Neural Network (RENN).
