Deep Poisson Factor Modeling

By Ricardo Henao, Zhe Gan, James Lu, Lawrence Carin. 2015

35 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Chunyuan Li
2. Zhe Gan
3. Hao Wang
4. Dit-Yan Yeung
5. Lawrence Carin
6. Dinghan Shen
7. Yunchen Pu
8. Changyou Chen

Citations in the corpus

• Natural-Parameter Networks: A Class of Probabilistic Neural Networks (2016, methodology): “For example, in gamma distributions p ( x | c, d ) = Γ( c ) − 1 d c x c − 1 exp( − dx ) , we use proxy natural parameters ( c, d ) during computation rather than the natural parameters ( c − 1 , − d ) .”
• Topic Compositional Neural Language Model (2017, background): “, 2005), learning deep architectures (Henao et al., 2015; Zhou et al., 2015), among many others.”
• WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling (2018, methodology): “To be consistent with previous settings (Gan et al., 2015; Henao et al., 2015; Cong et al., 2017a), no precautions are taken in the Wikipedia downloading script to prevent a testing document from being downloaded into a mini-batch for training.”
• Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification (2016, methodology): “%) pSGLD 73.32 RMSprop 72.52 DPFM [25] 72.67 PFM [25] 72.11 OSM [65] 69.1 DocNADE [36] 68.4 RSM [28] 67.7 LDA [54] 65.7 We follow the standard bag-of-features paradigm [8], where we extract local geometric descriptors, specifically spectral graph wavelet signature (SGWS) [44], and aggregate them wit”
• Towards Better Representations with Deep/Bayesian Learning (2018, background): “…al., 2017b; Pu et al., 2018b; Yuan et al., 2017, 2018; Chapfuwa et al., 2018), sigmoid belief networks (Gan et al., 2015b,c,a; Zhang et al., 2016a; Henao et al., 2015a), and their extensions to sequential data generation (Fan et al., 2016b; Zhang et al., 2016b; Su et al., 2017; Gan et al.,…”