libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models

By Joris M. Mooij. 2010

310 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, 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 Artificial Intelligence

1. Sergey Levine
2. Percy Liang
3. Mohit Bansal
4. Luke S. Zettlemoyer
5. Simon Lacoste-Julien
6. Yoshua Bengio
7. Hilbert J. Kappen
8. Noah A. Smith

In Computer Networks and Communications

1. Matthew R. Walter
2. Seth J. Teller
3. Vicenç Gómez
4. Simon Lacoste-Julien
5. Richard Dearden
6. Nick Hawes
7. Jason Eisner
8. Rahul G. Krishnan

In Probabilistic & Bayesian Methods

1. Jason Eisner
2. Veselin Stoyanov
3. Hilbert J. Kappen
4. David A. Sontag
5. Rahul G. Krishnan
6. Simon Lacoste-Julien
7. Richard Dearden
8. Nick Hawes

Citations in the corpus

• Barrier Frank-Wolfe for Marginal Inference (2015, methodology): “1(d) depicts for a single RBM, optimizing overMδ realizes significant gains in the upper bound on logZ which improves with updates to ρ.”
• Optimal control as a graphical model inference problem (2009, positive): “In this case, we can find the exact solution via the junction tree (JT) algorithm (Lauritzen and Spiegelhalter 1988; Mooij 2010).”
• Learning spatial-semantic representations from natural language descriptions and scene classifications (2014, methodology): “The distribution over the semantic layer is maintained using a factor graph [19] that is conditioned on the topology for each particle.”
• Message Passing Algorithms for Scalable Multitarget Tracking (2018, unknown): “This is true even though the MESP and JTree algorithms were implemented using a C++ library [146] whereas SPADA was implemented in MATLAB.”
• PyStruct: learning structured prediction in python (2014, unknown): “, 2013), LibDAI (Mooij, 2010) and others.”
• Learning Discriminative Fisher Kernels (2011, methodology): “The inference procedures were implemented using libDAI (Mooij, 2010).”
• Robot task planning and explanation in open and uncertain worlds (2015, methodology): “approximate, and uses the fast loopy belief propagation procedure [31].”
• Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor (2015, methodology): “.5(c)) using connected component analysis [17] and perform individual inference algorithm per each subgraph in parallel. If a subgraph is composed of more than one node, we use junctiontree algorithm [21,26] to obtain the solution for corresponding subgraph. Otherwise, we choose the best hypothesis ”
• Robot task planning and explanation in open and uncertain worlds (2017, methodology): “In our work, the underlying inference is approximate, and uses the fast loopy belief propagation procedure [31].”
• Empirical Risk Minimization of Graphical Model Parameters Given Approximate Inference, Decoding, and Model Structure (2011, methodology): “We implemented our algorithm in the libDAI framework (Mooij, 2010) extending the implementation of Eaton and Ghahramani (2009) (see Related Work Section).”
• Convergent Decomposition Solvers for Tree-reweighted Free Energies (2011, unknown): “We used libDAI (Mooij, 2010) to generate instances.”