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Found 6 papers, 1 papers with code
Projective Integral Updates for High-Dimensional Variational Inference
When the basis spans univariate quadratics in each parameter, feasible densities are Gaussian and the projective integral updates yield quasi-Newton variational Bayes (QNVB).
Variational Kalman Filtering with Hinf-Based Correction for Robust Bayesian Learning in High Dimensions
In this paper, we address the problem of convergence of sequential variational inference filter (VIF) through the application of a robust variational objective and Hinf-norm based correction for a linear Gaussian system.
Adaptive n-ary Activation Functions for Probabilistic Boolean Logic
Further, we represent belief tables using a basis that directly associates the number of nonzero parameters to the effective arity of the belief function, thus capturing a concrete relationship between logical complexity and efficient parameter representations.
Parsimonious Inference
Bayesian inference provides a uniquely rigorous approach to obtain principled justification for uncertainty in predictions, yet it is difficult to articulate suitably general prior belief in the machine learning context, where computational architectures are pure abstractions subject to frequent modifications by practitioners attempting to improve results.
Generalizing Information to the Evolution of Rational Belief
Rather than simply gauging uncertainty, information is understood in this theory to measure change in belief.
Generalized Canonical Polyadic Tensor Decomposition
Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing.
