We review our recent work, which includes several disparate approaches and tasks, all with the goal of using latent articulatory structure while learning discriminatively.

We propose a new modeling approach that is a generalization of gen-erative and discriminative models. The core idea is to use an implicitparameterization of a joint probability distribution by specifying only theconditional distributions. The proposed scheme combines the advantagesof both worlds – it can use powerful complex discriminative models asits parts, having at the s...

This paper proposes a statistical, treeto-tree model for producing translations. Two main contributions are as follows: (1) a method for the extraction of syntactic structures with alignment information from a parallel corpus of translations, and (2) use of a discriminative, featurebased model for prediction of these targetlanguage syntactic structures—which we call aligned extended projections...

The profusion of information retrieval effectiveness metrics has inspired the development of meta-evaluative criteria for choosing between them. One such criterion is discriminative power; that is, the proportion of system pairs whose difference in effectiveness is found statistically significant. Studies of discriminative power frequently find normalized discounted cumulative gain (nDCG) to be...

The acquired distinctiveness of cues ;: the role of discriminative verbal responses in facilitating the acquisition of discriminative motor responses. Rossman, Irma L., "The acquired distinctiveness of cues ;: the role of discriminative verbal responses in facilitating the acquisition of discriminative motor responses." ().

Traditional supervised band selection (BS) methods mainly consider reducing the spectral redundancy to improve hyperspectral imagery (HSI) classification with class labels and pairwise constraints. A key observation is that pixels spatially close to each other in HSI have probably the same signature, while pixels further away from each other in the space have a high probability of belonging to ...

Multivariate extreme value theory assumes a multivariate domain of attraction condition for the distribution of a random vector necessitating that each component satisfy a marginal domain of attraction condition. Heffernan and Tawn (2004) and Heffernan and Resnick (2007) developed an approximation to the joint distribution of the random vector by conditioning that one of the components be extre...