The Hierarchical Hidden Markov Model: Analysis and Applications

By Shai Fine, Yoram Singer, Naftali Tishby. 1998

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Citations in the corpus

Deep Learning: Methods and Applications (2014, methodology): “Related models with greater technical depth and mathematical treatment can be found in [116] for HHMM and [271] for Layered HMM.”
Variable Computation in Recurrent Neural Networks (2016, methodology): “The hierarchical HMM model of (Fine et al., 1998) and (Murphy & Paskin, 2001) takes a more direct approach to representing multiple scales of processes.”
A tutorial survey of architectures, algorithms, and applications for deep learning (2014, methodology): “Relatedmodels with greater technical depth andmathematical treatment can be found in [83] for HHMM and [84] for Layered HMM.”
Activity recognition and abnormality detection with the switching hidden semi-Markov model (2005, methodology): “Notice that activities with shorter life spans, such as [3],[4] and [6], will naturally result in higher early detection rates.”
Stochastic variational inference (2012, background): “…(Ghahramani and Beal, 2000; Attias, 2000), latent Dirichlet allocation (Blei et al., 2003), hidden Markov models (and many variants) (Rabiner, 1989; Fine et al., 1998; Fox et al., 2011b; Paisley and Carin, 2009), Kalman ﬁl-ters (and many variants) (Kalman, 1960; Fox et al., 2011a), factorial…”
Procedural Content Generation via Machine Learning (PCGML) (2017, methodology): “be utilized in a generative role, including n-grams, Markov models, autoencoders, and others [8], [9], [10].”
Understanding Video Events: A Survey of Methods for Automatic Interpretation of Semantic Occurrences in Video (2009, background): “In hierarchical Markov models (HHMMs) [83], [84], each possible state is represented by a lower level HMM.”
Conditional random fields for activity recognition (2007, background): “[27,80], although it results in models where exact inference is intractable.”
Parsing Videos of Actions with Segmental Grammars (2014, methodology): “Most related to us are hierarchical semi-Markov models and hierarchical HMMs (HHMMs) [5, 29, 18].”
Contextual LSTM (CLSTM) models for Large scale NLP tasks (2016, methodology): “These include models that capture the content structure using Hidden Markov Models (HMMs) [3], or semantic parsing techniques to identify the underlying meanings in text segments [29].”
Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model (2005, methodology): “The HHMM, first introduced in [8], extends the traditional HMM [18] in a hierarchic manner to include a hierarchy of hidden states.”
A General Method for Robust Bayesian Modeling (2015, background): “Non-exchangeable models only contain global variables, such as time series models (Rabiner, 1989; Fine et al., 1998; Fox et al., 2011; Paisley and Carin, 2009) and models for network analysis (Airoldi, 2007; Airoldi et al.”
Real-time human action recognition based on depth motion maps (2013, background): “The computational complexity for HMM is O(NhH (2)) [29], where Nh denotes the total number of states and H the length of the observation sequence.”
How the brain might work: a hierarchical and temporal model for learning and recognition (2008, background): “Hierarchical Hidden Markov Models (HHMMs) [29], are another class of hierarchical models that share some properties with HTMs.”