Article-Journal

We present a Forney-style factor graph representation for the class of multivariate autoregressive models with exogenous inputs, and propose an online Bayesian parameter-identification procedure based on message-passing within this graph.

We propose a Bayesian multiple model with an online model selection strategy to dynamically represent the latent motion modal from early observations. Each sub-model integrates a variational Kalman filter and Gated Recurrent Unit (GRU) neural network, enabling the estimation of time-varying transition coefficients and the process noise specific to different motion modalities.

We propose a probabilistic graph model and perform message passing to infer the hidden avoidance intent of ships during encounters.

An adaptive model-predictive controller that balances driving the system to a goal state and seeking system observations that are informative with respect to the parameters of a nonlinear autoregressive exogenous model.

Active Inference (AIF) is a framework that can be used both to describe information processing in naturally intelligent systems, such …

Time-varying autoregressive (TVAR) models are widely used for modeling of non-stationary signals. Unfortunately, online joint adaptation of both states and parameters in these models remains a challenge. In this paper, we represent the TVAR model by a factor graph and solve the inference problem by automated message passing-based inference for states and parameters.

Consider a domain-adaptive supervised learning setting, where a classifier learns from labeled data in a source domain and unlabeled …

In a broad range of fields it may be desirable to reuse a supervised classification algorithm and apply it to a new data set. However, …

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can …

OBJECTIVES: The most tedious and time-consuming task in medical additive manufacturing (AM) is image segmentation. The aim of the …