Probabilistic models with unknown objects

By Stuart J. Russell, Michael I. Jordan, Brian Milch. 2006

81 citations recorded by the source corpus.

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Citations in the corpus

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models (2016, background): “These models range from highly speciﬁed (Milch et al., 2005; Mansinghka et al., 2013), to partially speciﬁed (Zhu & Mumford, 2006; Roux et al., 2011; Heess et al., 2011; Eslami & Williams, 2014; Tang et al., 2013; 2014), to largely unspeciﬁed (Hinton, 2002; Salakhutdinov & Hin-ton, 2009; Eslami et…”
Bean Machine: A Declarative Probabilistic Programming Language For Efficient Programmable Inference (2020, methodology): “The semantics of Bean Machine are very similar to those established in the BLOG paper.”
Probabilistic machine learning and artificial intelligence (2015, background): “In return for this restriction, inference in such languages can be much faster than for the more general languages 56 , such as IBAL 57 , BLOG 58 , Church 59 , Figaro 60 , Venture 61 , and Anglican 62 .”
A Compilation Target for Probabilistic Programming Languages (2014, background): “Note that in this paper we do not show how to compile any existing probabilistic programming languages (i.e. IBAL (Pfeffer, 2001), BLOG (Milch et al., 2007), Church (Goodman et al., 2008), Figaro (Pfef-fer, 2009), Venture (Mansinghka et al., 2013), or Anglican (Wood et al., 2014)) to this intermedia”
Practical probabilistic programming with monads (2015, background): “Many different solutions were proposed to extend probabilistic programming systems to potentially inﬁnite models, such as combining graphical models with ﬁrst-order logic, as in BLOG (Milch et al. 2005), Markov Logic Networks (Domingos and Richardson 2004), and ProbLog (Kimmig et al. 2011).”
Unifying logic and probability (2015, unknown): “Moreover, by adding irrelevant objects to a scenario, one can change the model’s predictions for a given query to an arbitrary extent [19, 16].”
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs (2009, methodology): “Unlike BLOG, which uses a complex, highly-indexed data structure that must be updated during inference, we instead specify this connectivity imperatively: factor template objects have methods (e.g., unroll1, unroll2, etc., one for each factor argument) that find the factor's other variable neighbors”