Poster
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
Jianshu Chen · Ji He · Yelong Shen · Lin Xiao · Xiaodong He · Jianfeng Gao · Xinying Song · Li Deng

Thu Dec 10th 11:00 AM -- 03:00 PM @ 210 C #23 #None

We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model using Back Propagation (i.e., BP-sLDA), which maximizes the posterior probability of the prediction variable given the input document. Different from traditional variational learning or Gibbs sampling approaches, the proposed learning method applies (i) the mirror descent algorithm for maximum a posterior inference and (ii) back propagation over a deep architecture together with stochastic gradient/mirror descent for model parameter estimation, leading to scalable and end-to-end discriminative learning of the model. As a byproduct, we also apply this technique to develop a new learning method for the traditional unsupervised LDA model (i.e., BP-LDA). Experimental results on three real-world regression and classification tasks show that the proposed methods significantly outperform the previous supervised topic models, neural networks, and is on par with deep neural networks.

Author Information

Jianshu Chen (Microsoft Research, Redmond, W)
Ji He (University Washington)
Yelong Shen (Microsoft Research, Redmond, WA)
Lin Xiao (Microsoft)
Xiaodong He (Microsoft Research, Redmond, WA)
Jianfeng Gao (Microsoft Research, Redmond, WA)
Xinying Song (Microsoft Research, Redmond, WA)
Li Deng (MSR)

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