The Infinite Factorial Hidden Markov Model

By Jurgen Van Gael, Yee Whye Teh, Zoubin Ghahramani. 2008

112 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. Yoshua Bengio
2. Ruslan Salakhutdinov
3. Zhe Gan
4. Sergey Levine
5. Armand Joulin
6. Quoc V. Le
7. Trevor Darrell
8. Mohit Bansal

Citations in the corpus

• Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects (2018, methodology): “The object appearance/persistence/disappearance model in SQAIR is reminiscent of the Markov Indian buffet process (MIBP) (Gael et al., 2009), another BNP model.”
• Variable Computation in Recurrent Neural Networks (2016, methodology): “The Factorial HMM model of (Ghahramani & Jordan, 1997) (and its infinite extension in (Gael et al., 2008)) use parallel interacting hidden states to model concurrent processes.”
• Sharing Features among Dynamical Systems with Beta Processes (2009, methodology): “Other recent approaches to Bayesian nonparametric representations of time series include the HDP- HMM [2] [4] [5] [21] and the infinite factorial HMM [24].”
• A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2021, methodology): “[47] proposed the non-parametric FHMM, called iFHMM, by introducing a new stochastic process for latent feature representation of time series.”