FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs

By Andrew McCallum, Karl Schultz, Sameer Singh. 2009

244 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Probabilistic & Bayesian Methods

Citations in the corpus

An Introduction to Probabilistic Programming (2018, neutral): “, 2010a), Factorie (McCallum et al., 2009), Edward (Tran et al.”
Structural-RNN: Deep Learning on Spatio-Temporal Graphs (2015, methodology): “On the other hand, to keep the overall mixture scalable but not lose the essential learning capacity, we utilize “factor sharing” (aka clique templates [54, 42, 53]) and allow the factors with similar semantic functions to share an RNN.”
Venture: a higher-order probabilistic programming platform with programmable inference (2014, background): “As with FACTORIE and Markov Logic, these languages have interesting and useful properties, but have thus far not yielded compact descriptions of many useful classes of probabilistic generative models from domains such as statistics and robotics.”
Augur: Data-Parallel Probabilistic Modeling (2014, background): “In general, our results show that once the problem is large enough we can amortize Augur’s startup cost of model compilation to CUDA, nvcc compilation to a GPU binary, and copying the data to and from the GPU.”
Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations (2011, methodology): “Mintz et al. (2009) used Freebase facts to train 100 relational extractors on Wikipedia.”
Logical Induction (2016, background): “The related work in this field is extensive, including (but not limited to) work on probabilistic programming (Vajda 1972; McCallum, Schultz, and Singh 2009; Wood, Meent, and Mansinghka 2014; De Raedt and Kimmig 2015); probabilistic inductive logic programming (Muggleton and Watanabe 2014; De Raedt…”
Entity query feature expansion using knowledge base links (2014, methodology): “The entity mentions detected by Factorie are linked to the knowledge base 4http://people.cs.umass.edu/~vdang/ranklib. html using our state-of-the-art entity linking system, KB Bridge [11], which is trained on the TAC KBP entity linking data from 2009- 2012.”
Generating Efficient MCMC Kernels from Probabilistic Programs (2014, methodology): “It takes a Church program as input and traces its execution to remove computation overhead.”
Video Relationship Reasoning Using Gated Spatio-Temporal Energy Graph (2019, methodology): “Thanks to the recent advances in graphical models [33, 34, 21], deep neural networks [22, 35], and tensor factorization [14], our workaround is to parametrize and approximate f as f θ with learnable parameters θ as follows:”
AutoMan: a platform for integrating human-based and digital computation (2012, methodology): “It has been used to implement a variety of sublanguages, from a declarative syntax for probabilistic models to a BASIC interpreter [13, 24].”
Measure Transformer Semantics for Bayesian Machine Learning (2011, methodology): “FACTORIE [32] is a Scala library for explicitly constructing factor grap hs.”
Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference (2012, background): “Another possible direction is to introduce elements from imperative programming languages such as conditioning and looping (McCallum et al., 2009).”
Applications of Probabilistic Programming (Master's thesis, 2015) (2016, background): “Other languages and implementations also endorse declarative definitions of probabilistic models and include IBAL (Pfeffer, 2001), 4 Stan (Stan Development Team, 2014), BLOG (Milch et al., 2007), BUGS (Lunn et al., 2009), FACTORIE (McCallum et al., 2009), Markov Logic networks (Richardson and Doming”
Grammatical structures for word-level sentiment detection (2012, methodology): “This was implemented by changing the proposal distribution used by the FACTORIE framework (McCallum et al., 2009).”
Unifying logic and probability (2015, unknown): “Moreover, a logical query can be evaluated incrementally in each world visited, usually in constant time per world, rather than recomputing it from scratch [30, 18].”
An Introduction to Conditional Random Fields (2010, unknown): “Imperatively defined factor graphs [76] use the”