<commit_before>#Created by Kbib version 0.6.5
#Last modified: Fri May 21 15:08:57 2010

@book{A,
	title = {Handbook of Mathematical Functions},
	publisher = {Dover Publications},
	year = {1972},
	editor = {<NAME> and <NAME>},
	edition = {10th printing},
	lockkey = {Y}
}

@book{AmHeritage2000,
	author = {American Heritage Staff},
	title = {The American Heritage® Dictionary of the English Language},
	publisher = {Houghton Mifflin Company},
	year = 2000,
	edition = {Fourth Edition},
}

@inproceedings{Anderson2012,
	author = {<NAME>},
	title = {Dimensions of Morphological Complexity},
	booktitle = {Understanding and measuring morphological complexity},
	year = 2012,
	pages = {},
	editor = {<NAME>, <NAME>, <NAME> },
	url = {http:// xxx yale.edu/dimensions_revised.pdf}
}

@book{Ash1965,
   author = {<NAME>},
   title = {Information Theory},
   publisher = {Dover Publications},
   year = 1965,
   isbn = {0-486-66521-6},
   lockkey = {Y}
}

@article{Baez2009,
	title = {Physics, Topology, Logic and Computation: A Rosetta Stone},
	author = {<NAME> and <NAME>},
	journal = {Arxiv/abs/0903.0340},
	year = 2009,
	url = {http://math.ucr.edu/home/baez/rosetta.pdf},
	lockkey = {Y}
}

@article{Bartunov2015,
	author = {<NAME>, <NAME>, <NAME>, Dmitry},
	title = {Breaking Sticks and Ambiguities with Adaptive Skip-gram},
	year = 2015,
	journal = {arXiv/arXiv:1502.07257v2}
}

@article{Bell1998,
	author = {<NAME>},
	title = {The Co-Information Lattice},
	journal = {Somewhere or other},
	year = {1998},
	url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.457.2244&rep=rep1&type=pdf},
	doi = {10.1.1.457.2244}
}

@article{Bengio2003,
	author = {<NAME>. and <NAME>. and <NAME>.},
	title = {A neural probabilistic language model.},
	year = 2003,
	journal = {Journal of Machine Learning Research},
	volume = 3,
	pages = {1137-1155}
}

@article{Coecke2010,
	title = {Quantum Links Let Computers Read},
	author = {<NAME>},
	journal = {New Scientist},
	year = 2010,
	month = December,
	url = {http://www.cs.ox.ac.uk/people/bob.coecke/NewScientist.pdf},
	lockkey = {Y}
}

@article{Cohen2010,
	author = {<NAME> and <NAME>},
	title = {Covariance in Unsupervised Learning of Probabilistic Grammars},
	journal = {Journal of Machine Learning Research },
	year = 2010,
	volume = {11},
	pages = {3117-3151},
	lockkey = {Y}
}

@inproceedings{Domingos2006,
	author = {<NAME> and <NAME> and <NAME> and <NAME>},
	title = {Unifying logical and statistical AI},
	booktitle = {AAAI'06: Proceedings of the 21st national conference on Artificial intelligence},
	pages = {2--7},
	year = 2006,
	publisher = {AAAI Press},
	abstract = {Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the former, and statistical AI on the latter. Markov logic combines the two by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Inference algorithms for Markov logic draw on ideas from satisfiability, Markov chain Monte Carlo and knowledge-based model construction. Learning algorithms are based on the voted perceptron, pseudo-likelihood and inductive logic programming. Markov logic has been successfully applied to problems in entity resolution, link prediction, information extraction and others, and is the basis of the open-source Alchemy system.},
	isbn = {978-1-57735-281-5},
	location = {Boston, Massachusetts},
	lockkey = {Y}
}

@book{Eisenbud2000,
	title = {The Geometry of Schemes},
	author = {<NAME> and <NAME>},
	publisher = {Springer},
	year = 2000,
	isbn = {0-387-98637-5},
	lockkey = {Y}
}

@article{Ferrer2006,
	author = {<NAME>},<commit_msg>Add a new bib entry<commit_after>#Created by Kbib version 0.6.5
#Last modified: Fri May 21 15:08:57 2010

@book{A,
	title = {Handbook of Mathematical Functions},
	publisher = {Dover Publications},
	year = {1972},
	editor = {<NAME> and <NAME>},
	edition = {10th printing},
	lockkey = {Y}
}

@book{AmHeritage2000,
	author = {American Heritage Staff},
	title = {The American Heritage® Dictionary of the English Language},
	publisher = {Houghton Mifflin Company},
	year = 2000,
	edition = {Fourth Edition},
}

@inproceedings{Anderson2012,
	author = {<NAME>},
	title = {Dimensions of Morphological Complexity},
	booktitle = {Understanding and measuring morphological complexity},
	year = 2012,
	pages = {},
	editor = {<NAME>, <NAME>, <NAME> },
	url = {http:// xxx yale.edu/dimensions_revised.pdf}
}

@book{Ash1965,
   author = {<NAME>},
   title = {Information Theory},
   publisher = {Dover Publications},
   year = 1965,
   isbn = {0-486-66521-6},
   lockkey = {Y}
}

@article{Baez2009,
	title = {Physics, Topology, Logic and Computation: A Rosetta Stone},
	author = {<NAME> and <NAME>},
	journal = {Arxiv/abs/0903.0340},
	year = 2009,
	url = {http://math.ucr.edu/home/baez/rosetta.pdf},
	lockkey = {Y}
}

@article{Bartunov2015,
	author = {<NAME> and Kondrashkin, Dmitry and Osokin, <NAME> Vetrov, Dmitry},
	title = {Breaking Sticks and Ambiguities with Adaptive Skip-gram},
	year = 2015,
	journal = {arXiv/arXiv:1502.07257v2}
}

@article{Bell1998,
	author = {<NAME>},
	title = {The Co-Information Lattice},
	journal = {Somewhere or other},
	year = {1998},
	url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.457.2244&rep=rep1&type=pdf},
	doi = {10.1.1.457.2244}
}

@article{Bengio2003,
	author = {<NAME>. and <NAME>. and <NAME>.},
	title = {A neural probabilistic language model.},
	year = 2003,
	journal = {Journal of Machine Learning Research},
	volume = 3,
	pages = {1137-1155}
}

@article{Coecke2010,
	title = {Quantum Links Let Computers Read},
	author = {<NAME>},
	journal = {New Scientist},
	year = 2010,
	month = December,
	url = {http://www.cs.ox.ac.uk/people/bob.coecke/NewScientist.pdf},
	lockkey = {Y}
}

@article{Cohen2010,
	author = {<NAME> and <NAME>},
	title = {Covariance in Unsupervised Learning of Probabilistic Grammars},
	journal = {Journal of Machine Learning Research },
	year = 2010,
	volume = {11},
	pages = {3117-3151},
	lockkey = {Y}
}

@inproceedings{Domingos2006,
	author = {<NAME> and <NAME> and <NAME> and Richardson, <NAME> <NAME>},
	title = {Unifying logical and statistical AI},
	booktitle = {AAAI'06: Proceedings of the 21st national conference on Artificial intelligence},
	pages = {2--7},
	year = 2006,
	publisher = {AAAI Press},
	abstract = {Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the former, and statistical AI on the latter. Markov logic combines the two by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Inference algorithms for Markov logic draw on ideas from satisfiability, Markov chain Monte Carlo and knowledge-based model construction. Learning algorithms are based on the voted perceptron, pseudo-likelihood and inductive logic programming. Markov logic has been successfully applied to problems in entity resolution, link prediction, information extraction and others, and is the basis of the open-source Alchemy system.},
	isbn = {978-1-57735-281-5},
	location = {Boston, Massachusetts},
	lockkey = {Y}
}

@unpublished{Dzuigaite2015,
	author = {<NAME> and <NAME>},
	title = {Neural Network Matrix Factorization},
	note = {ArXiv abs/1511.06443},
	year = 2015,
	url = {https://arxiv.org/abs/1511.06443},
}

@book{Eisenbud2000,
	title = {The Geometry of Schemes},
	author = {<NAME> and <NAME>},
	publisher = {Springer},
	year = 2000,
	isbn = {0-387-98637-5},
	lockkey = {Y}
}

@article{Ferrer2006,
	author = {<NAME>},
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