Abstract: A computer-implemented method includes receiving an incorrect prediction output by a trained machine learning model, which has been trained using training data items. The method includes identifying a training data item used to train the model that is a cause of the incorrect prediction, by determining an impact on performance of the trained machine learning model associated with removing the training data item from the plurality of training data. The trained model can then be updated to remove the effect of the identified training data item, allowing the model to be automatically corrected in view of poor quality training data.

Abstract: A computer implemented method comprising: receiving a report on a condition of a human or animal subject, composed by a user based on a scan of the subject; inputting the current report and the scan into a trained machine learning model; and based on the report and the scan, the machine learning model generating one or more suggestions for updating the text of the report. The method further comprises causing a user interface to display to the user one or more suggestions for updating the text of the report, with each respective suggestion visually linked in the user interface to a corresponding subregion within at least one image of the scan based upon which the respective suggestion was generated.

Abstract: There is provided a method and system for modeling a data generating process. The method includes generating a dyadic Bayesian model including a pair of probabilistic functions representing a prior distribution and a sampling distribution, and modeling a data generating process based on the dyadic Bayesian model using observed data. The method includes generating a learner object for the dyadic Bayesian model. The method further includes training the dyadic Bayesian model with the learner object based on the observed data to produce a trained dyadic Bayesian model. The method also includes generating a posterior distribution over parameters based on the trained dyadic Bayesian model. The method also further includes generating a posterior predictive distribution based on the posterior distribution. The method also includes predicting an outcome of observable variables based on the posterior predictive distribution.

Abstract: A quantified belief propagation (QBP) algorithm receives as input an existentially quantified boolean formula (QBF) of existentially quantified boolean variables, universally quantified variables, and boolean operators. A tripartite graph is constructed, and includes (i) there-exists nodes that correspond to and represent the existentially quantified variables, (ii) for-all nodes that correspond to and represent the universally quantified variables, and (iii) sub-formula nodes that correspond to and represent sub-formulas of the QBF. A set of boolean values of the existentially quantified variables is found by (i) passing a first message from an arbitrary sub-formula node to an arbitrary for-all node, and (ii) in response, passing a second message from the arbitrary for-all node to the arbitrary sub-formula node.

Abstract: There is provided a method and system for modeling a data generating process. The method includes generating a dyadic Bayesian model including a pair of probabilistic functions representing a prior distribution and a sampling distribution, and modeling a data generating process based on the dyadic Bayesian model using observed data.