185 citations · 1.5k across the 88 of their papers we have counts for
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Earliness-Aware Deep Convolutional Networks for Early Time Series Classification
Wenlin Wang, Changyou Chen, Wenqi Wang +2
We present Earliness-Aware Deep Convolutional Networks (EA-ConvNets), an end-to-end deep learning framework, for early classification of time series data. Unlike most existing meth…
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
Zhe Gan, Chunyuan Li, Changyou Chen +3
Recurrent neural networks (RNNs) have shown promising performance for language modeling. However, traditional training of RNNs using back-propagation through time often suffers fro…
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators
Changyou Chen, Nan Ding, Lawrence Carin
Recent advances in Bayesian learning with large-scale data have witnessed emergence of stochastic gradient MCMC algorithms (SG-MCMC), such as stochastic gradient Langevin dynamics…
Stochastic Gradient MCMC with Stale Gradients
Changyou Chen, Nan Ding, Chunyuan Li +2
Stochastic gradient MCMC (SG-MCMC) has played an important role in large-scale Bayesian learning, with well-developed theoretical convergence properties. In such applications of SG…
Twitter-Network Topic Model: A Full Bayesian Treatment for Social Network and Text Modeling
Kar Wai Lim, Changyou Chen, Wray Buntine
Twitter data is extremely noisy -- each tweet is short, unstructured and with informal language, a challenge for current topic modeling. On the other hand, tweets are accompanied b…
Nonparametric Bayesian Topic Modelling with the Hierarchical Pitman-Yor Processes
Kar Wai Lim, Wray Buntine, Changyou Chen +1
The Dirichlet process and its extension, the Pitman-Yor process, are stochastic processes that take probability distributions as a parameter. These processes can be stacked up to f…