Evaluating Unsupervised Representation Learning for Detecting Stances of Fake News

Our goal is to evaluate the usefulness of unsupervised representation learning techniques for detecting stances of Fake News. Therefore we examine several pre-trained language models with respect to their performance on two Fake News related data sets, both consisting of instances with a headline, an associated news article and the stance of the article towards the respective headline. Specifically, the aim is to understand how much hyperparameter tuning is necessary when fine-tuning the pre-trained architectures, how well transfer learning works in this specific case of stance detection and how sensitive the models are to changes in hyperparameters like batch size, learning rate (schedule), sequence length as well as the freezing technique. The results indicate that the computationally more expensive autoregression approach of XLNet (Yanget al., 2019) is outperformed by BERT-based models, notably by RoBERTa (Liu et al., 2019).While the learning rate seems to be the most important hyperparameter, experiments with different freezing techniques indicate that all evaluated architectures had already learned powerful language representations that pose a good starting point for fine-tuning them

Test Oracles Using Statistical Methods

Abstract: The oracle problem is addressed for random testing and testing of randomized software. The presented Statistical Oracle is a Heuristic Oracle using statistical methods, especially statistical tests. The Statistical Oracle is applicable in case there are explicit formulae for the mean, the distribution, and so on, of characteristics computable from the test result. However, the present paper only deals with the mean. As with the Heuristic Oracle, the decision of the Statistical Oracle is not always correct. An example from image analysis is shown, where the Statistical Oracle has successfully been applied.

On the Estimation of Integrated Covariance Functions of Stationary Random Fields

For stationary vector-valued random fields on the asymptotic covariance matrix for estimators of the mean vector can be given by integrated covariance functions. To construct asymptotic confidence intervals and significance tests for the mean vector, non-parametric estimators of these integrated covariance functions are required. Integrability conditions are derived under which the estimators of the covariance matrix are mean-square consistent. For random fields induced by stationary Boolean models with convex grains, these conditions are expressed by sufficient assumptions on the grain distribution. Performance issues are discussed by means of numerical examples for Gaussian random fields and the intrinsic volume densities of planar Boolean models with uniformly bounded grains. Copyright (c) 2009 Board of the Foundation of the Scandinavian Journal of Statistics.

random polyconvex

Algorithms for the computation of th