Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data

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EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction (2019, methodology): “ch document is modeled as a mixture of topics and each word in a 4 Preprint. Under review. document relates to one or more topics. While LDA has been recently combined with recurrent neural networks (Zaheer et al., 2017), the methodology remains different. LDA learns the parameters of a probabilisti”
Deep Factors for Forecasting (2019, methodology): “This makes the state transition non-linearly determined by the RNN. VRNNs are also used in Latent LSTM Allocation (LLA) (Zaheer et al., 2017) and State-Space LSTM (SSL) (Zheng et al., 2017).”
State Space LSTM Models with Particle MCMC Inference (2017, methodology): “To test the ﬂexibility of SSL, we begin with inference using synthetic data.”
Topic Modeling Using Latent Dirichlet allocation (2021, methodology): “LDA was integrated with neural network [96, 157, 172], and collaborative filtering.”
Deep State Space Models for Time Series Forecasting (2018, methodology): “They were later used in Latent LSTM Allocation [29] and State-Space LSTM [30].”