We investigate the task of medical concept coreference resolution in clinical text using two semi-supervised methods, co-training and multi-view learning with posterior regularization. By extracting semantic and temporal features of medical concepts found in clinical text, we create conditionally independent data views; co-training MaxEnt classifiers on this data works almost as well as supervi...

Abstract. In this paper we analyze iterative regularization with the Bregman distance of the total variation semi norm. Moreover, we prove existence of a solution of the corresponding flow equation as introduced in [8] in a functional analytical setting using methods from convex analysis. The results are generalized to variational denoising methods with L-norm fit-to-data terms and Bregman dist...

Unregularized deep neural networks (DNNs) can be easily overfit with a limited sample size. We argue that this is mostly due to the disriminative nature of DNNs which directly model the conditional probability (or score) of labels given the input. The ignorance of input distribution makes DNNs difficult to generalize to unseen data. Recent advances in regularization techniques, such as pretrain...

Super-resolution (SR) aims to overcome the ill-posed conditions of image acquisition. SR facilitates scene recognition from low-resolution image(s). Generally assumes that high and low resolution images share similar intrinsic geometries. Various approaches have tried to aggregate the informative details of multiple low-resolution images into a high-resolution one. In this paper, we present a n...

The graph Laplacian regularization term is usually used in semi-supervised representation learning to provide structure information for a model f(X). However, with the recent popularity of neural networks (GNNs), directly encoding A into model, i.e., f(A, X), has become more common approach. While we show that brings little-to-no benefit existing GNNs, and propose simple but non-trivial variant...

A method for describing the quantum kink states in the semi-classical limit of several (1+1)-dimensional field theoretical models is developed. We use the generalized zeta function regularization method to compute the one-loop quantum correction to the masses of the kink in the sine-Gordon and cubic sinh-Gordon models and another two P(φ)2 systems with polynomial self-interactions.

In this paper, a semi-supervised modeling framework that combines feature-based (x) data and graph-based (G) data for classification/regression of the response Y is presented. In this semi-supervised setting, Y is observed for a subset of the observations (labeled) and missing for the remainder (unlabeled). The Propagated Scoring algorithm proposed for fitting this model is a semi-supervised fi...

Most existing semi-supervised learning methods are based on the smoothness assumption that data points in the same high density region should have the same label. This assumption, though works well in many cases, has some limitations. To overcome this problems, we introduce into semi-supervised learning the classic low-dimensionality embedding assumption, stating that most geometric information...

We consider the problem of labeling a partially labeled graph. This setting may arise in a number of situations from survey sampling to information retrieval to pattern recognition in manifold settings. It is also of potential practical importance, when the data is abundant, but labeling is expensive or requires human assistance. Our approach develops a framework for regularization on such grap...

We investigate the convergence of a forward-backward-forward proximal-type algorithm with inertial and memory effects when minimizing the sum of a nonsmooth function with a smooth one in the absence of convexity. The convergence is obtained provided an appropriate regularization of the objective satisfies the KurdykaLojasiewicz inequality, which is for instance fulfilled for semi-algebraic func...