5 papers · 1 filter
A Differentiable Covariance Calculus for Linear Gaussian Bayesian Networks
Tadashi Wadayama
Linear Gaussian Bayesian networks, equivalently linear Gaussian structural equation models, recur across statistics, control, and communications; in the vector-valued setting that…
Mutual Information Optimization via K-Recursion and Automatic Differentiation for Linear Gaussian Wireless Networks
Tadashi Wadayama, Na Siqi
We present a differentiable framework for end-to-end mutual information (MI) optimization over linear Gaussian directed acyclic graphs (DAGs). The framework targets network-wide de…
Information Gradient for Directed Acyclic Graphs: A Score-based Framework for End-to-End Mutual Information Maximization
Tadashi Wadayama
This paper presents a general framework for end-to-end mutual information maximization in communication and sensing systems represented by stochastic directed acyclic graphs (DAGs)…
Information Gradient for Nonlinear Gaussian Channel with Applications to Task-Oriented Communication
Tadashi Wadayama
We propose a gradient-based framework for optimizing parametric nonlinear Gaussian channels via mutual information maximization. Leveraging the score-to-Fisher bridge (SFB) methodo…
Mutual Information Estimation via Score-to-Fisher Bridge for Nonlinear Gaussian Noise Channels
Tadashi Wadayama
We present a numerical method to evaluate mutual information (MI) in nonlinear Gaussian noise channels by using denoising score matching (DSM) learning for estimating the score fun…