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Found 16 papers, 12 papers with code
Information-seeking polynomial NARX model-predictive control through expected free energy minimization
We propose 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.
Variational Bayes for robust radar single object tracking
We address object tracking by radar and the robustness of the current state-of-the-art methods to process outliers.
Online system identification in a Duffing oscillator by free energy minimisation
Online system identification is the estimation of parameters of a dynamical system, such as mass or friction coefficients, for each measurement of the input and output signals.
Bayesian joint state and parameter tracking in autoregressive models
We address the problem of online Bayesian state and parameter tracking in autoregressive (AR) models with time-varying process noise variance.
The Data Representativeness Criterion: Predicting the Performance of Supervised Classification Based on Data Set Similarity
In a broad range of fields it may be desirable to reuse a supervised classification algorithm and apply it to a new data set.
A cross-center smoothness prior for variational Bayesian brain tissue segmentation
Here we present a smoothness prior that is fit to segmentations produced at another medical center.
A review of domain adaptation without target labels
Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure.
An introduction to domain adaptation and transfer learning
Domain adaptation and transfer learning are sub-fields within machine learning that are concerned with accounting for these types of changes.
Learning an MR acquisition-invariant representation using Siamese neural networks
Generalization of voxelwise classifiers is hampered by differences between MRI-scanners, e. g. different acquisition protocols and field strengths.
Target Robust Discriminant Analysis
In practice, the data distribution at test time often differs, to a smaller or larger extent, from that of the original training data.
Effects of sampling skewness of the importance-weighted risk estimator on model selection
For sample selection bias settings, and for small sample sizes, the importance-weighted risk estimator produces overestimates for datasets in the body of the sampling distribution, i. e. the majority of cases, and large underestimates for data sets in the tail of the sampling distribution.
Robust importance-weighted cross-validation under sample selection bias
Cross-validation under sample selection bias can, in principle, be done by importance-weighting the empirical risk.
MR Acquisition-Invariant Representation Learning
Due to this acquisition related variation, classifiers trained on data from a specific scanner fail or under-perform when applied to data that was acquired differently.
Target contrastive pessimistic risk for robust domain adaptation
In domain adaptation, classifiers with information from a source domain adapt to generalize to a target domain.
On Regularization Parameter Estimation under Covariate Shift
This paper identifies a problem with the usual procedure for L2-regularization parameter estimation in a domain adaptation setting.
Feature-Level Domain Adaptation
Our empirical evaluation of FLDA focuses on problems comprising binary and count data in which the transfer can be naturally modeled via a dropout distribution, which allows the classifier to adapt to differences in the marginal probability of features in the source and the target domain.
