Strongly augmented contrastive clustering

Weakly-Supervised Learning with Cost-Augmented Contrastive Estimation

We generalize contrastive estimation in two ways that permit adding more knowledge to unsupervised learning. The first allows the modeler to specify not only the set of corrupted inputs for each observation, but also how bad each one is. The second allows specifying structural preferences on the latent variable used to explain the observations. They require setting additional hyperparameters, w...

Vertex Clustering of Augmented Graph Streams

In this paper we propose a graph stream clustering algorithm with a unified similarity measure on both structural and attribute properties of vertices, with each attribute being treated as a vertex. Unlike others, our approach does not require an input parameter for the number of clusters, instead, it dynamically creates new sketch-based clusters and periodically merges existing similar cluster...

Clustering via Content-Augmented Stochastic Blockmodels

Much of the data being created on the web contains interactions between users and items. Stochastic blockmodels, and other methods for community detection and clustering of bipartite graphs, can infer latent user communities and latent item clusters from this interaction data. These methods, however, typically ignore the items’ contents and the information they provide about item clusters, desp...

Contrastive Topic and/or Contrastive Focus

Contrastive Focus and Contrastive Topics in Greek

This paper puts forward the idea that in Greek contrast is an independent feature, represented in syntax by a preverbal position. Contrast can be assigned to diierent sorts of entities, topics and foci, once a set/list of contrasting alternatives/entities has been introduced or directly implied by the discourse. Evidence for an independent treatment of contrast is provided by similarities in th...