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- Chong Wang, David M. Blei
- KDD
- 2011

Researchers have access to large online archives of scientific articles. As a consequence, finding relevant papers has become more difficult. Newly formed online communities of researchers sharing citations provides a new way to solve this problem. In this paper, we develop an algorithm to recommend scientific articles to users of an online community. Our… (More)

- Matthew D. Hoffman, David M. Blei, Chong Wang, John William Paisley
- Journal of Machine Learning Research
- 2013

The distinction between local and global variables will be important for us to develop online inference. In Bayesian statistics, for example, think of β as parameters with a prior and z1:n as hidden variables which are individual to each observation. In a Bayesian mixture of Gaussians the global variables β are the mixture components and mixture… (More)

- Chong Wang, David M. Blei, Fei-fei Li
- 2009 IEEE Conference on Computer Vision and…
- 2009

Image classification and annotation are important problems in computer vision, but rarely considered together. Intuitively, annotations provide evidence for the class label, and the class label provides evidence for annotations. For example, an image of class highway is more likely annotated with words “road,” “car,” and… (More)

Probabilistic topic models are a popular tool for the unsupervised analysis of text, providing both a predictive model of future text and a latent topic representation of the corpus. Practitioners typically assume that the latent space is semantically meaningful. It is used to check models, summarize the corpus, and guide exploration of its contents.… (More)

- Dario Amodei, Rishita Anubhai, +31 authors Zhenyao Zhu
- ICML
- 2016

We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech–two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different… (More)

- Chong Wang, Miao Xiong, Qi Zhou, Yong Yu
- ESWC
- 2007

Providing a natural language interface to ontologies will not only offer ordinary users the convenience of acquiring needed information from ontologies, but also expand the influence of ontologies and the semantic web consequently. This paper presents PANTO, a Portable nAtural laNguage inTerface to Ontologies, which accepts generic natural language queries… (More)

The hierarchical Dirichlet process (HDP) is a Bayesian nonparametric mixed membership model—each data point is modeled with a collection of components of different proportions. Though powerful, the HDP makes an assumption that the probability of a component being exhibited by a data point is positively correlated with its proportion within that data point.… (More)

- Chong Wang, Weiqiang Ren, Kaiqi Huang, Tieniu Tan
- ECCV
- 2014

Localizing objects in cluttered backgrounds is a challenging task in weakly supervised localization. Due to large object variations in cluttered images, objects have large ambiguity with backgrounds. However, backgrounds contain useful latent information, e.g., the sky for aeroplanes. If we can learn this latent information, object-background ambiguity can… (More)

- Chong Wang, John William Paisley, David M. Blei
- AISTATS
- 2011

The hierarchical Dirichlet process (HDP) is a Bayesian nonparametric model that can be used to model mixed-membership data with a potentially infinite number of components. It has been applied widely in probabilistic topic modeling, where the data are documents and the components are distributions of terms that reflect recurring patterns (or “topics”) in… (More)

- Willie Neiswanger, Chong Wang, Eric P. Xing
- UAI
- 2014

Communication costs, resulting from synchronization requirements during learning, can greatly slow down many parallel machine learning algorithms. In this paper, we present a parallel Markov chain Monte Carlo (MCMC) algorithm in which subsets of data are processed independently, with very little communication. First, we arbitrarily partition data onto… (More)