The Indian Buffet Process: An Introduction and Review

By T. Griffiths, Zoubin Ghahramani. 2011

457 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Bayesian Nonparametric Federated Learning of Neural Networks (2019, methodology): “Several extensions to the IBP (and the equivalent BBP) have been developed, see Grifﬁths & Ghahramani (2011) for a review.”
Bayesian Structure Adaptation for Continual Learning (2019, methodology): “As described before, to adaptively infer the number of nodes in each hidden layer, we use the IBP prior (Grifﬁths & Ghahramani, 2011), whose truncated stick-breaking process (Doshi et al., 2009) construction for each entry of B is as follows for d ∈ 1 , ..., D , where D denotes the number of input…”
Deep Multi-task Representation Learning: A Tensor Factorisation Approach (2016, methodology): “, 2012] models W using linear factor analysis with Indian Buffet Process [Griffiths and Ghahramani, 2011] prior on S.”
Bayesian group factor analysis with structured sparsity (2016, methodology): “, 2014) puts an Indian buffet process (IBP) prior (Griffiths and Ghahramani, 2011) on the factors, inducing element-wise sparsity, and an ARD prior on the variance of the loadings.”
Graph-Sparse LDA: A Topic Model with Structured Sparsity (2014, methodology): “Formally, we replace the previous LDA generative process with the following process that introduces ˜ w in as the concept word behind observed word w in : w in | ˜ w in , P ∼ Discrete ( P ˜ w in ) where ⊙ is the element-wise Hadamard product and IBP is the Indian Buffet Process [13].”
Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations (2013, methodology): “Our generative model uses Indian Buffet Processes [8] to model what latent parameters are relevant for a particular set of dynamics and Gaussian processes [9] to model the dynamics functions.”
Better Optimism By Bayes: Adaptive Planning with Rich Models (2014, background): “A more radical extension would be to something closer to an Indian buffet process (Griffiths and Ghahramani 2011), in which the whole collection of subtasks also share a measure of structure; this should lead to solutions with collaboration among a set of expert solvers.”
Probabilistic machine learning and artificial intelligence (2015, methodology): “Elaborating the social network modelling example, an IBP-based model allows each person to belong to some subset of a large number of potential communities (for example, as defined by different families, workplaces, schools, hobbies, and so on) rather than a single community, and the probability of ”
Scalable inference of topic evolution via models for latent geometric structures (2018, background): “(15) in [10]) with mi denoting the popularity of global topic i.”
Indian Buffet Neural Networks for Continual Learning (2019, methodology): “The inference for IBP has been performed in several ways, including Gibbs sampling [33, 13], particle filtering [34], slice sampling [35], and using variational inference [12].”
Spectral Methods for Indian Buffet Process Inference (2014, methodology): “Then the probability of generating a particular matrix Z is given by [18]”
Discovering latent causes in reinforcement learning (2015, background): “In the Indian buffet process [57], customers enter the buffet and sample each dish (latent cause) with probability proportional to the number of other customers who have previously sampled the dish.”
General Latent Feature Models for Heterogeneous Datasets (2017, methodology): “In particular, these works assume that databases contain only either continuous data, usually modeled as Gaussian variables [12, 28], or discrete, that can be either modeled by discrete likelihoods [18, 24, 11] or simply treated as Gaussian variables [25, 2, 28].”
Networks beyond pairwise interactions: structure and dynamics (2020, methodology): “The Indian Buffet Process is defined in similar terms but allows for multiple set memberships.”