In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Input: Consider Input: Probabilistic linear discriminant analysis (PLDA) is a popular normalization approach for the i-vector model, and has delivered state-of-the-art performance in speaker recognition. A potential problem of the PLDA model, however, is that it essentially assumes Gaussian distributions over speaker vectors, which is not always true in practice. Additionally, the objective function is not directly related to the goal of the task, e.g., discriminating true speakers and imposters. In this paper, we propose a max-margin metric learning approach to solve the problems. It learns a linear transform with a criterion that the margin between target and imposter trials are maximized. Experiments conducted on the SRE08 core test show that compared to PLDA, the new approach can obtain comparable or even better performance, though the scoring is simply a cosine computation.

Output: Max-Margin Metric Learning for Speaker Recognition


Input: Consider Input: In the mixture models problem it is assumed that there are K distributions θ1, . . . , θK and one gets to observe a sample from a mixture of these distributions with unknown coefficients. The goal is to associate instances with their generating distributions, or to identify the parameters of the hidden distributions. In this work we make the assumption that we have access to several samples drawn from the same K underlying distributions, but with different mixing weights. As with topic modeling, having multiple samples is often a reasonable assumption. Instead of pooling the data into one sample, we prove that it is possible to use the differences between the samples to better recover the underlying structure. We present algorithms that recover the underlying structure under milder assumptions than the current state of art when either the dimensionality or the separation is high. The methods, when applied to topic modeling, allow generalization to words not present in the training data.

Output: Using multiple samples to learn mixture models


Input: Consider Input: The efficiency of inference in both the Hugin and, most notably, the Shafer-Shenoy archi­ tectures can be improved by exploiting the independence relations induced by the incom­ ing messages of a clique. That is, the mes­ sage to be sent from a clique can be com­ puted via a factorization of the clique poten­ tial in the form of a junction tree. In this pa­ per we show that by exploiting such nested junction trees in the computation of messages both space and time costs of the conventional propagation methods may be reduced. The paper presents a structured way of exploit­ ing the nested junction trees technique to achieve such reductions. The usefulness of the method is emphasized through a thor­ ough empirical evaluation involving ten large real-world Bayesian networks and the Hugin inference algorithm.


Output: Nested Junction Trees