This assumption significantly reduces the complexity of the optimization problem. Instead of learning one large, entangled distribution, we learn several smaller, independent ones. While this ...

Abstract: Sparse diagnosis techniques for antenna arrays provide an efficient approach to fault diagnosis by leveraging the sparse nature of faulty elements. In practical scenarios, an unknown ...

Abstract: We propose a control barrier function (CBF) formulation for enforcing equality and inequality constraints in variational inference. The key idea is to define a barrier functional on the ...

Deep neural networks are good at extracting low-dimensional subspaces (latent spaces) that represent the essential features inside a high-dimensional dataset. Deep generative models represented by ...

At first glance, Bayes’ Theorem appears deceptively simple—it provides a clear framework for updating our beliefs about an unknown quantity after observing new data. The core idea is to combine prior ...

This work considers a class of canonical neural networks comprising rate coding models, wherein neural activity and plasticity minimise a common cost function—and plasticity is modulated with a ...

ERTVI is an open-source code used for quantifying resistivity distributions and associated uncertainties in electrical resistivity tomography (ERT) using variational inference (especially SVGD or ...

This paper proposes Synonymous Variational Inference, a novel variational inference that first theoretically explains the core reason for the divergence term’s ...