Learning Systems of Concepts with an Infinite Relational Model

By Charles Kemp, Josh Tenenbaum, T. Griffiths, Takeshi Yamada, Naonori Ueda. 2006

793 citations recorded by the source corpus.

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Citations in the corpus

Irreflexive and Hierarchical Relations as Translations (2013, methodology): “Kemp et al. (2006) considered a non-parametric Bayesian extension of the stochastic block-model allowing to automatically infer the number of latent clusters; Kok & Domingos (2007) introduced clustering in Markov-Logic networks; Sutskever et al. (2009) used a non-parametric Bayesian clustering of…”
Church: a language for generative models (2008, neutral): “This basic structure may be used to easily describe more complicated mixture models, such as the continuous-data infinite relational model (IRM) from [7].”
Embedding Entities and Relations for Learning and Inference in Knowledge Bases (2014, background): “…representations of entities and relations, including tensor/matrix factorization (Singh & Gordon, 2008; Nickel et al., 2011; 2012), Bayesian clustering framework (Kemp et al., 2006; Sutskever et al., 2009), and neural networks (Paccanaro & Hinton, 2001; Bordes et al., 2013a;b; Socher et al., 2013).”
A Review of Relational Machine Learning for Knowledge Graphs (2015, neutral): “…instance, [59] showed that RESCAL provides comparable or better relationship prediction results on a number of small benchmark datasets compared to Markov Logic Networks (with structure learning) [66], the Inﬁnite (Hidden) Relational model [67, 68], and Bayesian Clustered Tensor Factorization [69].”
Holographic Embeddings of Knowledge Graphs (2015, background): “Hence, learning from and with relational knowledge representations has long been considered an important task in artiﬁcial intelligence (see e.g., Getoor and Taskar (2007); Muggleton (1991); Gentner (1983); Kemp et al. (2006); Xu et al. (2006); Richardson and Domingos (2006)).”
Exploiting compositionality to explore a large space of model structures (2012, methodology): “By applying Rule 2(b), the clustering model can be extended to co-clustering (Kemp et al., 2006), where the columns (attributes) form clusters as well.”
Multiscale Community Blockmodel for Network Exploration (2011, methodology): “To begin, methods such as Girvan and Newman (2002), Hoﬀ et al. (2002), Handcock et al. (2007), Krause et al. (2003) and Guimera and Amaral (2005) cannot discover disassor-tative communities characterized by weak within-community and strong cross-community interactions.”
Semantically Smooth Knowledge Graph Embedding (2015, background): “The third group embeds factorized representations of entities and relations into a nonparametric Bayesian clustering framework, so as to obtain more interpretable embed-dings (Kemp et al., 2006; Sutskever et al., 2009).”
Statistical predicate invention (2007, methodology): “In our experiments, we compare MRC with IRM ( Kemp et al., 2006 ) and MLN structure learning (Kok & Domingos, 2005).”
Topic and Role Discovery in Social Networks (2005, background): “For example, in addition to determining that a node-degree distribution is heavy-tailed, we can also find those particular nodes with an inordinately high number of connections, or with connections to a particularly well-connected subset (group or block) of the network (Nowicki & Snijders, 2001; Kem”
Differentiable Reasoning on Large Knowledge Bases and Natural Language (2019, unknown): “We report the re-sults of experiments on benchmark datasets — Countries (Bouchard, Singh, and Trouillon 2015), Nations, UMLS, and Kinship (Kemp et al. 2006) — following the same evaluation protocols as Rockt¨aschel and Riedel (2017).”
Mixed Membership Stochastic Blockmodels (2007, methodology): “…technique of Markov random networks (Frank and Strauss, 1986) have been used for link prediction (Taskar et al., 2003) and the traditional blockmodels have been extended to include nonparametric Bayesian priors (Kemp et al., 2004, 2006) and to integrate relations and text (McCallum et al., 2007).”
Modeling relational events via latent classes (2010, methodology): “In our experiments we compare our model to the IRM approach by using a single relation on three domains.”
Nonparametric Latent Feature Models for Link Prediction (2009, methodology): “See [6, 19, 20] for more details on the datasets.”
Generalized Latent Factor Models for Social Network Analysis (2011, methodology): “These models include: the latent class model [Nowicki and Snijders, 2001] and its extensions [Kemp et al., 2006; Airoldi et al., 2008], the latent distance model [Hoff et al.”