Tree-Structured Stick Breaking for Hierarchical Data

By Ryan P. Adams, Zoubin Ghahramani, Michael I. Jordan. 2010

175 citations recorded by the source corpus.

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Citations in the corpus

GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning (2021, background): “Hierarchical stick break (Adams et al., 2010) proposed a tree-based stick break for clustering as an alternative to the standard sequential stick break.”
On Nonparametric Guidance for Learning Autoencoder Representations (2011, methodology): “, 2010) and Bayesian nonparametric models (Adams et al., 2010).”
Hierarchical Semi-supervised Classification with Incomplete Class Hierarchies (2016, background): “Another set of techniques focus on completely unsupervised information extraction and ontology discovery [15, 4, 21, 10].”
Constrained Semi-supervised Learning in the Presence of Unanticipated Classes (2016, background): “Another set of techniques focus on completely unsupervised information extraction and ontology discovery [12, 53, 108, 126].”
Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition (2019, background): “[176] provides a robust for undetermined data order to image recognition.”
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling (2018, methodology): “Rather than sequentially partitioning the latent space using stick breaking, we use a treestructured stick breaking procedure (Adams et al., 2010) to partition the space.”
Bayesian Optimization and Semiparametric Models with Applications to Assistive Technology (2014, methodology): “, 2010], and Bayesian nonparametric modeling [Adams et al., 2010].”
Spectral Methods for Indian Buffet Process Inference (2014, background): “References [1] R.”