The paper presents the Bayesian analysis of a new model in software reliability, aimed to detect deterioration due to potential introduction of new bugs into the software during the development phase. Since the introduction of bugs is an unobservable process, latent variables are introduced to incorporate this characteristic into the proposed hidden Markov model. The state of the latent process...

In this paper, we propose to recognize unsuccessful activities (e.g., one tries to dress himself but fails), which have much more complex temporal structures, as we don’t know when the activity performer fails (which is called the point of failure in this paper). We develop a twolayer latent structural SVM model to tackle this problem: the first layer specifies the point of failure, and the sec...

In this paper, we propose a generative model, Temporal Generative Adversarial Nets (TGAN), which can learn a semantic representation of unlabeled videos, and is capable of generating videos. Unlike existing Generative Adversarial Nets (GAN)-based methods that generate videos with a single generator consisting of 3D deconvolutional layers, our model exploits two different types of generators: a ...

In this paper, we propose a latent multi-task learning algorithm to solve the multi-device indoor localization problem. Traditional indoor localization systems often assume that the collected signal data distributions are fixed, and thus the localization model learned on one device can be used on other devices without adaptation. However, by empirically studying the signal variation over differ...

As Web search engines play an important role in helping people find required information from massive Web data, the Web query classification (WQC) problem becomes an important research issue. Contrast to traditional classification problems, WQC is to classify Web queries into relevant categories of a Web taxonomy. In addition to the typical challenge of processing short and ambiguous queries, W...

People share various types of information including opinions on hot topics, bookmarking activity and rumors via online communities. To make it possible to predict future trends in online communities, it is important that we develop a model of information diffusion through social networks and a method for estimating its parameters. In this paper, we present a latent feature independent cascade m...

Although topic models designed for textual collections annotated with geographical meta-data have been previously shown to be effective at capturing vocabulary preferences of people living in different geographical regions, little is known about their utility for information retrieval in general or microblog retrieval in particular. In this work, we propose simple and scalable geographical late...

Although the visual perception of 3D shape from 2D images is a basic capability of human beings, it remains challenging to computers. Hence, one goal of vision research is to computationally understand and model the latent 3D scene from the captured images, and provide human-like visual system for machines. In this paper, we present a method that is capable of building a realistic 3D model for ...

In web search, latent semantic models have been proposed to bridge the lexical gap between queries and documents that is due to the fact that searchers and content creators often use different vocabularies and language styles to express the same concept. Modern search engines simply use the outputs of latent semantic models as features for a so-called global ranker. We argue that this is not op...