Machine Learning: Publications

Book chapters

• [1]

  Collapsed Variational Bayesian Inference for Hidden Markov Models

  Pengyu Wang and Phil Blunsom

  In AISTATS. 2013.

  Details about Collapsed Variational Bayesian Inference for Hidden Markov Models | BibTeX data for Collapsed Variational Bayesian Inference for Hidden Markov Models

• [2]

  Inference Strategies for Solving Semi−Markov Decision Processes

  Matthew Hoffman and Nando de Freitas

  Chapter 5. Pages 82–96. Hershey: IGI Global. 2012.

  Details about Inference Strategies for Solving Semi−Markov Decision Processes | BibTeX data for Inference Strategies for Solving Semi−Markov Decision Processes | Download (pdf) of Inference Strategies for Solving Semi−Markov Decision Processes

• [3]

  Bayesian Synchronous Grammar Induction

  Phil Blunsom‚ Trevor Cohn and Miles Osborne

  In D. Koller‚ D. Schuurmans‚ Y. Bengio and L. Bottou, editors, Advances in Neural Information Processing Systems 21. Pages 161–168. 2008.

  Details about Bayesian Synchronous Grammar Induction | BibTeX data for Bayesian Synchronous Grammar Induction

• [4]

  Bayesian Policy Learning with Trans−Dimensional MCMC

  Matthew Hoffman‚ Arnaud Doucet‚ Nando de Freitas and Ajay Jasra

  In J.C. Platt‚ D. Koller‚ Y. Singer and S. Roweis, editors, Advances in Neural Information Processing Systems 20. Pages 665–672. MIT Press, Cambridge‚ MA. 2007.

  Details about Bayesian Policy Learning with Trans−Dimensional MCMC | BibTeX data for Bayesian Policy Learning with Trans−Dimensional MCMC | Link to Bayesian Policy Learning with Trans−Dimensional MCMC

Journal papers

• [1]

  New Directions in Vector Space Models of Meaning

  Edward Grefenstette‚ Karl Moritz Hermann‚ Georgiana Dinu and Phil Blunsom

  In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. June, 2014.

  Details about New Directions in Vector Space Models of Meaning | BibTeX data for New Directions in Vector Space Models of Meaning | Download (pdf) of New Directions in Vector Space Models of Meaning

• [2]

  A Deep Architecture for Semantic Parsing

  Edward Grefenstette‚ Phil Blunsom‚ Nando de Freitas and Karl Moritz Hermann

  In Proceedings of the ACL 2014 Workshop on Semantic Parsing. June, 2014.

  Details about A Deep Architecture for Semantic Parsing | BibTeX data for A Deep Architecture for Semantic Parsing | Link to A Deep Architecture for Semantic Parsing

• [3]

  Self−Avoiding Random Dynamics on Integer Complex Systems

  Firas Hamze‚ Ziyu Wang and Nando de Freitas

  In ACM Transactions on Modelling and Computer Simulation. Vol. 23. No. 1. Pages 9:1–9:25. 2013.

  Details about Self−Avoiding Random Dynamics on Integer Complex Systems | BibTeX data for Self−Avoiding Random Dynamics on Integer Complex Systems | DOI (10.1145/2414416.2414790) | Link to Self−Avoiding Random Dynamics on Integer Complex Systems

• [4]

  Recurrent Convolutional Neural Networks for Discourse Compositionality

  Nal Kalchbrenner and Phil Blunsom

  In Proceedings of the 2013 Workshop on Continuous Vector Space Models and their Compositionality. 2013.

  Details about Recurrent Convolutional Neural Networks for Discourse Compositionality | BibTeX data for Recurrent Convolutional Neural Networks for Discourse Compositionality

• [5]

  "Not not bad" is not "bad": A distributional account of negation

  Karl Moritz Hermann‚ Edward Grefenstette and Phil Blunsom

  In Proceedings of the 2013 Workshop on Continuous Vector Space Models and their Compositionality. August, 2013.

  Details about "Not not bad" is not "bad": A distributional account of negation | BibTeX data for "Not not bad" is not "bad": A distributional account of negation | Link to "Not not bad" is not "bad": A distributional account of negation

• [6]

  On Sparse‚ Spectral and Other Parameterizations of Binary Probabilistic Models

  David Buchman‚ Mark W. Schmidt‚ Shakir Mohamed‚ David Poole and Nando de Freitas

  In Journal of Machine Learning Research − Proceedings Track for Artificial Intelligence and Statistics (AISTATS). Vol. 22. Pages 173–181. 2012.

  Details about On Sparse‚ Spectral and Other Parameterizations of Binary Probabilistic Models | BibTeX data for On Sparse‚ Spectral and Other Parameterizations of Binary Probabilistic Models | Link to On Sparse‚ Spectral and Other Parameterizations of Binary Probabilistic Models

• [7]

  Learning where to attend with deep architectures for image tracking

  Misha Denil‚ Loris Bazzani‚ Hugo Larochelle and Nando de Freitas

  In Neural Computation. Vol. 24. No. 8. Pages 2151–2184. 2012.

  Details about Learning where to attend with deep architectures for image tracking | BibTeX data for Learning where to attend with deep architectures for image tracking | DOI (10.1162/NECO_a_00312) | Link to Learning where to attend with deep architectures for image tracking

• [8]

  Inducing Tree−Substitution Grammars

  Trevor Cohn‚ Phil Blunsom and Sharon Goldwater

  In Journal of Machine Learning Research. Pages 3053–3096. 2010.

  Details about Inducing Tree−Substitution Grammars | BibTeX data for Inducing Tree−Substitution Grammars

• [9]

  Metrics for MT Evaluation: Evaluating Reordering

  Alexandra Birch‚ Phil Blunsom and Miles Osborne

  In Machine Translation. 2010.

  Details about Metrics for MT Evaluation: Evaluating Reordering | BibTeX data for Metrics for MT Evaluation: Evaluating Reordering

• [10]

  Monte Carlo techniques for phrase−based translation

  Abhishek Arun‚ Chris Dyer‚ Barry Haddow‚ Phil Blunsom‚ Adam Lopez and Philipp Koehn

  In Special Issue of Machine Translation Journal. 2010.

  Details about Monte Carlo techniques for phrase−based translation | BibTeX data for Monte Carlo techniques for phrase−based translation

• [11]

  An Introduction to MCMC for Machine Learning

  Christophe Andrieu‚ Nando de Freitas‚ Arnaud Doucet and Michael I. Jordan

  In Machine Learning. Vol. 50. No. 1−2. Pages 5−43. 2003.

  Details about An Introduction to MCMC for Machine Learning | BibTeX data for An Introduction to MCMC for Machine Learning | DOI (10.1023/A:1020281327116) | Link to An Introduction to MCMC for Machine Learning

Conference papers

• [1]

  Deep Fried Convnets

  Zichao Yang‚ Marcin Moczulski‚ Misha Denil‚ Nando de Freitas‚ Alexander J. Smola‚ Le Song and Ziyu Wang

  In ICCV. 2015.

  Details about Deep Fried Convnets | BibTeX data for Deep Fried Convnets | Link to Deep Fried Convnets

• [2]

  From Group to Individual Labels using Deep Features

  Dimitrios Kotzias‚ Misha Denil‚ Nando de Freitas and Padhraic Smyth

  In ACM SIGKDD. 2015.

  Details about From Group to Individual Labels using Deep Features | BibTeX data for From Group to Individual Labels using Deep Features | Download (pdf) of From Group to Individual Labels using Deep Features

• [3]

  Compositional Morphology for Word Representations and Language Modelling

  Jan A. Botha and Phil Blunsom

  In Proceedings of the 31st International Conference on Machine Learning (ICML). Beijing‚ China. June, 2014.

  *Award for best application paper*

  Details about Compositional Morphology for Word Representations and Language Modelling | BibTeX data for Compositional Morphology for Word Representations and Language Modelling | Download (pdf) of Compositional Morphology for Word Representations and Language Modelling

• [4]

  Bayesian Optimization with an Empirical Hardness Model for Approximate Nearest Neighbour Search

  Julieta Martinez‚ James Little and Nando de Freitas

  In IEEE Winter Conference on Applications of Computer Vision (WACV). 2014.

  Details about Bayesian Optimization with an Empirical Hardness Model for Approximate Nearest Neighbour Search | BibTeX data for Bayesian Optimization with an Empirical Hardness Model for Approximate Nearest Neighbour Search | Download (pdf) of Bayesian Optimization with an Empirical Hardness Model for Approximate Nearest Neighbour Search

• [5]

  Narrowing the Gap: Random Forests In Theory and In Practice

  Misha Denil‚ David Matheson and Nando de Freitas

  In International Conference on Machine Learning (ICML). 2014.

  Details about Narrowing the Gap: Random Forests In Theory and In Practice | BibTeX data for Narrowing the Gap: Random Forests In Theory and In Practice | Download (pdf) of Narrowing the Gap: Random Forests In Theory and In Practice

• [6]

  Linear and Parallel Learning for Markov Random Fields

  Yariv Dror Mizrahi‚ Misha Denil and Nando de Freitas

  In International Conference on Machine Learning (ICML). 2014.

  Details about Linear and Parallel Learning for Markov Random Fields | BibTeX data for Linear and Parallel Learning for Markov Random Fields | Download (pdf) of Linear and Parallel Learning for Markov Random Fields

• [7]

  Bayesian Multi−Scale Optimistic Optimization

  Ziyu Wang‚ Babak Shakibi‚ Lin Jin and Nando de Freitas

  In Artificial Intelligence and Statistics (AISTATS). Pages 1005−1014. 2014.

  Details about Bayesian Multi−Scale Optimistic Optimization | BibTeX data for Bayesian Multi−Scale Optimistic Optimization | Download (pdf) of Bayesian Multi−Scale Optimistic Optimization

• [8]

  Modelling the Lexicon in Unsupervised Part of Speech Induction

  Gregory Dubbin and Phil Blunsom

  In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. Pages 116–125. Gothenburg‚ Sweden. April, 2014. Association for Computational Linguistics.

  Details about Modelling the Lexicon in Unsupervised Part of Speech Induction | BibTeX data for Modelling the Lexicon in Unsupervised Part of Speech Induction | Link to Modelling the Lexicon in Unsupervised Part of Speech Induction

• [9]

  Linguistic steganography on Twitter: hierarchical language modeling with manual interaction

  Alex Wilson‚ Phil Blunsom and Andrew D Ker

  In &T/SPIEIS Electronic Imaging. Pages 902803–902803. International Society for Optics and Photonics. 2014.

  Details about Linguistic steganography on Twitter: hierarchical language modeling with manual interaction | BibTeX data for Linguistic steganography on Twitter: hierarchical language modeling with manual interaction

• [10]

  Semantic Frame Identification with Distributed Word Representations

  Karl Moritz Hermann‚ Dipanjan Das‚ Jason Weston and Kuzman Ganchev

  In Proceedings of ACL. June, 2014.

  Details about Semantic Frame Identification with Distributed Word Representations | BibTeX data for Semantic Frame Identification with Distributed Word Representations | Link to Semantic Frame Identification with Distributed Word Representations

• [11]

  Multilingual Models for Compositional Distributional Semantics

  Karl Moritz Hermann and Phil Blunsom

  In Proceedings of ACL. June, 2014.

  Details about Multilingual Models for Compositional Distributional Semantics | BibTeX data for Multilingual Models for Compositional Distributional Semantics | Link to Multilingual Models for Compositional Distributional Semantics

• [12]

  Multilingual Distributed Representations without Word Alignment

  Karl Moritz Hermann and Phil Blunsom

  In Proceedings of ICLR. April, 2014.

  Details about Multilingual Distributed Representations without Word Alignment | BibTeX data for Multilingual Distributed Representations without Word Alignment | Link to Multilingual Distributed Representations without Word Alignment

• [13]

  Adaptive Hamiltonian and Riemann Manifold Monte Carlo Samplers

  Ziyu Wang‚ Shakir Mohamed and Nando de Freitas

  In International Conference on Machine Learning (ICML). Pages 1462–1470. 2013.

  JMLR &CPW 28 (3): 1462–1470‚ 2013

  Details about Adaptive Hamiltonian and Riemann Manifold Monte Carlo Samplers | BibTeX data for Adaptive Hamiltonian and Riemann Manifold Monte Carlo Samplers | Download (pdf) of Adaptive Hamiltonian and Riemann Manifold Monte Carlo Samplers

• [14]

  Herded Gibbs Sampling

  Luke Bornn‚ Yutian Chen‚ Nando de Freitas‚ Mareija Eskelin‚ Jing Fang and Max Welling

  In International Conference on Learning Representations (ICLR). 2013.

  Details about Herded Gibbs Sampling | BibTeX data for Herded Gibbs Sampling | Link to Herded Gibbs Sampling

• [15]

  Predicting Parameters in Deep Learning

  Misha Denil‚ Babak Shakibi‚ Laurent Dinh‚ Marc'Aurelio Ranzato and Nando de Freitas

  In Advances in Neural Information Processing Systems (NIPS). 2013.

  Details about Predicting Parameters in Deep Learning | BibTeX data for Predicting Parameters in Deep Learning | Download (pdf) of Predicting Parameters in Deep Learning

• [16]

  Consistency of Online Random Forests

  Misha Denil‚ David Matheson and Nando de Freitas

  In International Conference on Machine Learning (ICML). Pages 1256–−1264. 2013.

  JMLR &CPW 28 (3): 1256–1264‚ 2013

  Details about Consistency of Online Random Forests | BibTeX data for Consistency of Online Random Forests | Download (pdf) of Consistency of Online Random Forests

• [17]

  Adaptor Grammars for Learning Non−Concatenative Morphology

  Jan A. Botha and Phil Blunsom

  In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Pages 345−356. Seattle‚ Washington‚ USA. October, 2013. Association for Computational Linguistics.

  Details about Adaptor Grammars for Learning Non−Concatenative Morphology | BibTeX data for Adaptor Grammars for Learning Non−Concatenative Morphology | Download (pdf) of Adaptor Grammars for Learning Non−Concatenative Morphology

• [18]

  Collapsed Variational Bayesian Inference for PCFGs

  Pengyu Wang and Phil Blunsom

  In Proceedings of the Seventeenth Conference on Computational Natural Language Learning. Pages 173–182. Sofia‚ Bulgaria. August, 2013. Association for Computational Linguistics.

  Details about Collapsed Variational Bayesian Inference for PCFGs | BibTeX data for Collapsed Variational Bayesian Inference for PCFGs | Link to Collapsed Variational Bayesian Inference for PCFGs

• [19]

  Identification and Mitigation of Non−line−of−sight conditions Using Received Signal Strength

  Zhuoling Xiao‚ Hongkai Wen‚ Andrew Markham‚ Niki Trigoni‚ Phil Blunsom and and Jeff Frolik

  In Proceedings of the 9th IEEE International Conference on Wireless and Mobile Computing‚ Networking and Communications (WiMob 2013). Lyon‚ France. October, 2013.

  Details about Identification and Mitigation of Non−line−of−sight conditions Using Received Signal Strength | BibTeX data for Identification and Mitigation of Non−line−of−sight conditions Using Received Signal Strength

• [20]

  Recurrent Continuous Translation Models

  Nal Kalchbrenner and Phil Blunsom

  Seattle. October, 2013. Association for Computational Linguistics.

  Details about Recurrent Continuous Translation Models | BibTeX data for Recurrent Continuous Translation Models

• [21]

  The Role of Syntax in Vector Space Models of Compositional Semantics

  Karl Moritz Hermann and Phil Blunsom

  In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Pages 894–904. Sofia‚ Bulgaria. August, 2013. Association for Computational Linguistics.

  Details about The Role of Syntax in Vector Space Models of Compositional Semantics | BibTeX data for The Role of Syntax in Vector Space Models of Compositional Semantics | Download (pdf) of The Role of Syntax in Vector Space Models of Compositional Semantics

• [22]

  On Assessing the Accuracy of Positioning Systems in Indoor Environments (BEST PAPER)

  H. Wen‚ Z. Xiao‚ N. Trigoni and P. Blunsom

  In 10th European Conference on Wireless Sensor Networks (EWSN'13). Ghent‚ Belgium. 2013.

  Details about On Assessing the Accuracy of Positioning Systems in Indoor Environments (BEST PAPER) | BibTeX data for On Assessing the Accuracy of Positioning Systems in Indoor Environments (BEST PAPER) | Link to On Assessing the Accuracy of Positioning Systems in Indoor Environments (BEST PAPER)

• [23]

  A Systematic Bayesian Treatment of the IBM Alignment Models

  Yarin Gal and Phil Blunsom

  In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics. 2013.

  Details about A Systematic Bayesian Treatment of the IBM Alignment Models | BibTeX data for A Systematic Bayesian Treatment of the IBM Alignment Models | Link to A Systematic Bayesian Treatment of the IBM Alignment Models

• [24]

  A Machine Learning Perspective on Predictive Coding with PAQ8

  Byron Knoll and Nando de Freitas

  In Data Compression Conference (DCC). Pages 377–386. 2012.

  Details about A Machine Learning Perspective on Predictive Coding with PAQ8 | BibTeX data for A Machine Learning Perspective on Predictive Coding with PAQ8 | DOI (10.1109/DCC.2012.44) | Link to A Machine Learning Perspective on Predictive Coding with PAQ8

• [25]

  Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations

  Nando de Freitas‚ Alex Smola and Masrour Zoghi

  In International Conference on Machine Learning (ICML). 2012.

  Details about Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations | BibTeX data for Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations | Link to Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations

• [26]

  An Unsupervised Ranking Model for Noun−Noun Compositionality

  Karl Moritz Hermann‚ Phil Blunsom and Stephen Pulman

  In *SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task‚ and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012). Pages 132–141. Montréal‚ Canada. 2012. Association for Computational Linguistics.

  Details about An Unsupervised Ranking Model for Noun−Noun Compositionality | BibTeX data for An Unsupervised Ranking Model for Noun−Noun Compositionality | Download  (pdf) of An Unsupervised Ranking Model for Noun−Noun Compositionality | Link to An Unsupervised Ranking Model for Noun−Noun Compositionality

• [27]

  Learning Semantics and Selectional Preference of Adjective−Noun Pairs

  Karl Moritz Hermann‚ Chris Dyer‚ Phil Blunsom and Stephen Pulman

  In *SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task‚ and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012). Pages 70–74. Montréal‚ Canada. 2012. Association for Computational Linguistics.

  Details about Learning Semantics and Selectional Preference of Adjective−Noun Pairs | BibTeX data for Learning Semantics and Selectional Preference of Adjective−Noun Pairs | Download  (pdf) of Learning Semantics and Selectional Preference of Adjective−Noun Pairs | Link to Learning Semantics and Selectional Preference of Adjective−Noun Pairs

• [28]

  The pascal challenge on grammar induction

  Douwe Gelling‚ Trevor Cohn‚ Phil Blunsom and Joao Graça

  In Proceedings of the NAACL−HLT Workshop on the Induction of Linguistic Structure. Pages 64–80. Association for Computational Linguistics. June, 2012.

  Details about The pascal challenge on grammar induction | BibTeX data for The pascal challenge on grammar induction

• [29]

  Unsupervised Bayesian Part of Speech Inference with Particle Gibbs

  Gregory Dubbin and Phil Blunsom

  In N. Cristianini P. Flach T. De Bie, editor, Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). Bristol‚ UK. September, 2012. Springer.

  Details about Unsupervised Bayesian Part of Speech Inference with Particle Gibbs | BibTeX data for Unsupervised Bayesian Part of Speech Inference with Particle Gibbs

• [30]

  Bayesian Language Modelling of German Compounds

  Jan A. Botha‚ Chris Dyer and Phil Blunsom

  In Proceedings of the 24th International Conference on Computational Linguistics (COLING). Pages 341–356. Mumbai‚ India. December, 2012. The COLING 2012 Organizing Committee.

  Details about Bayesian Language Modelling of German Compounds | BibTeX data for Bayesian Language Modelling of German Compounds | Download (pdf) of Bayesian Language Modelling of German Compounds

• [31]

  A Bayesian Model for Learning SCFGs with Discontiguous Rules

  Abby Levenberg‚ Chris Dyer and Phil Blunsom

  In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Pages 223–232. Association for Computational Linguistics. July, 2012.

  Details about A Bayesian Model for Learning SCFGs with Discontiguous Rules | BibTeX data for A Bayesian Model for Learning SCFGs with Discontiguous Rules

• [32]

  Unsupervised part of speech inference with particle filters

  Gregory Dubbin and Phil Blunsom

  In Proceedings of the NAACL−HLT Workshop on the Induction of Linguistic Structure. Pages 47–54. Association for Computational Linguistics. 2012.

  Details about Unsupervised part of speech inference with particle filters | BibTeX data for Unsupervised part of speech inference with particle filters

• [33]

  Toward the Implementation of a Quantum RBM

  Misha Denil and Nando de Freitas

  In NIPS 2011 Deep Learning and Unsupervised Feature Learning Workshop. 2011.

  Details about Toward the Implementation of a Quantum RBM | BibTeX data for Toward the Implementation of a Quantum RBM | Download (pdf) of Toward the Implementation of a Quantum RBM

• [34]

  On Autoencoders and Score Matching for Energy Based Models

  Kevin Swersky‚ Marc'Aurelio Ranzato‚ David Buchman‚ Benjamin Marlin and Nando Freitas

  In Lise Getoor and Tobias Scheffer, editors, Proceedings of the 28th International Conference on Machine Learning (ICML−11). Pages 1201–1208. New York‚ NY‚ USA. June, 2011. ACM.

  Details about On Autoencoders and Score Matching for Energy Based Models | BibTeX data for On Autoencoders and Score Matching for Energy Based Models | Link to On Autoencoders and Score Matching for Energy Based Models

• [35]

  A Hierarchical Pitman−Yor Process HMM for Unsupervised Part of Speech Induction

  Phil Blunsom and Trevor Cohn

  In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Pages 865–874. Portland‚ Oregon‚ USA. June, 2011. Association for Computational Linguistics.

  Details about A Hierarchical Pitman−Yor Process HMM for Unsupervised Part of Speech Induction | BibTeX data for A Hierarchical Pitman−Yor Process HMM for Unsupervised Part of Speech Induction | Link to A Hierarchical Pitman−Yor Process HMM for Unsupervised Part of Speech Induction

• [36]

  Deep Learning of Invariant Spatio−Temporal Features from Video

  Bo Chen‚ Jo−Anne Ting‚ Ben Marlin and Nando de Freitas

  In NIPS 2010 Deep Learning and Unsupervised Feature Learning Workshop. 2010.

  Details about Deep Learning of Invariant Spatio−Temporal Features from Video | BibTeX data for Deep Learning of Invariant Spatio−Temporal Features from Video | Download (pdf) of Deep Learning of Invariant Spatio−Temporal Features from Video

• [37]

  BLOCKED INFERENCE IN BAYESIAN TREE SUBSTITUTION GRAMMARS

  Trevor Cohn and Phil Blunsom

  In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Uppsala‚ Sweden. 2010.

  Details about BLOCKED INFERENCE IN BAYESIAN TREE SUBSTITUTION GRAMMARS | BibTeX data for BLOCKED INFERENCE IN BAYESIAN TREE SUBSTITUTION GRAMMARS

• [38]

  cdec: A Decoder‚ Alignment‚ and Learning framework for finite−state and context−free translation models

  Chris Dyer‚ Adam Lopez‚ Juri Ganitkevitch‚ Johnathan Weese‚ Ferhan Ture‚ Phil Blunsom‚ Hendra Setiawan‚ Vladimir Eidelman and Philip Resnik

  In Proceedings of the ACL 2010 System Demonstrations. Pages 7–12. 2010.

  Details about cdec: A Decoder‚ Alignment‚ and Learning framework for finite−state and context−free translation models | BibTeX data for cdec: A Decoder‚ Alignment‚ and Learning framework for finite−state and context−free translation models

• [39]

  Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing

  Phil Blunsom and Trevor Cohn

  In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. Pages 1204–1213. Cambridge‚ MA. October, 2010. Association for Computational Linguistics.

  Details about Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing | BibTeX data for Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing | Link to Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing

• [40]

  A Note on the Implementation of Hierarchical Dirichlet Processes

  Phil Blunsom‚ Trevor Cohn‚ Sharon Goldwater and Mark Johnson

  In Proceedings of the ACL−IJCNLP 2009 Conference Short Papers. Pages 337–340. Suntec‚ Singapore. August, 2009. Association for Computational Linguistics.

  Details about A Note on the Implementation of Hierarchical Dirichlet Processes | BibTeX data for A Note on the Implementation of Hierarchical Dirichlet Processes | Link to A Note on the Implementation of Hierarchical Dirichlet Processes

• [41]

  A Bayesian Model of Syntax−Directed Tree to String Grammar Induction

  Trevor Cohn and Phil Blunsom

  In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. Pages 352–361. Singapore. August, 2009. Association for Computational Linguistics.

  Details about A Bayesian Model of Syntax−Directed Tree to String Grammar Induction | BibTeX data for A Bayesian Model of Syntax−Directed Tree to String Grammar Induction | Link to A Bayesian Model of Syntax−Directed Tree to String Grammar Induction

• [42]

  A Gibbs Sampler for Phrasal Synchronous Grammar Induction

  Phil Blunsom‚ Trevor Cohn‚ Chris Dyer and Miles Osborne

  In Proc. of the Joint conference of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL/IJCNLP−09). Pages 782–790. Singapore. August, 2009.

  Details about A Gibbs Sampler for Phrasal Synchronous Grammar Induction | BibTeX data for A Gibbs Sampler for Phrasal Synchronous Grammar Induction

• [43]

  Inducing compact but accurate tree−substitution grammars

  Trevor Cohn‚ Sharon Goldwater and Phil Blunsom

  In NAACL '09: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Pages 548–556. Morristown‚ NJ‚ USA. 2009. Association for Computational Linguistics.

  Details about Inducing compact but accurate tree−substitution grammars | BibTeX data for Inducing compact but accurate tree−substitution grammars

• [44]

  Monte Carlo inference and maximization for phrase−based translation

  Abhishek Arun‚ Chris Dyer‚ Barry Haddow‚ Phil Blunsom‚ Adam Lopez and Philipp Koehn

  In CoNLL '09: Proceedings of the Thirteenth Conference on Computational Natural Language Learning. Pages 102–110. Morristown‚ NJ‚ USA. 2009. Association for Computational Linguistics.

  Details about Monte Carlo inference and maximization for phrase−based translation | BibTeX data for Monte Carlo inference and maximization for phrase−based translation

• [45]

  A Discriminative Latent Variable Model for Statistical Machine Translation

  Phil Blunsom‚ Trevor Cohn and Miles Osborne

  In Proc. of the 46th Annual Conference of the Association for Computational Linguistics: Human Language Technologies (ACL−08:HLT). Pages 200–208. Columbus‚ Ohio. June, 2008.

  Details about A Discriminative Latent Variable Model for Statistical Machine Translation | BibTeX data for A Discriminative Latent Variable Model for Statistical Machine Translation | Link to A Discriminative Latent Variable Model for Statistical Machine Translation

• [46]

  Probabilistic Inference for Machine Translation

  Phil Blunsom and Miles Osborne

  In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing. Pages 215–223. Honolulu‚ Hawaii. October, 2008.

  Details about Probabilistic Inference for Machine Translation | BibTeX data for Probabilistic Inference for Machine Translation

• [47]

  SMC Samplers for Bayesian Optimal Nonlinear Design

  Hendrik Kuck‚ N. de Freitas and Arnaud Doucet

  In IEEE Nonlinear Statistical Signal Processing Workshop. Pages 99–102. 2006.

  Details about SMC Samplers for Bayesian Optimal Nonlinear Design | BibTeX data for SMC Samplers for Bayesian Optimal Nonlinear Design | Download (pdf) of SMC Samplers for Bayesian Optimal Nonlinear Design

• [48]

  Discriminative Word Alignment with Conditional Random Fields

  Phil Blunsom and Trevor Cohn

  In Proc. of the 44th Annual Meeting of the ACL and 21st International Conference on Computational Linguistics (COLING/ACL−2006). Pages 65–72. Sydney‚ Australia. July, 2006.

  Details about Discriminative Word Alignment with Conditional Random Fields | BibTeX data for Discriminative Word Alignment with Conditional Random Fields | Link to Discriminative Word Alignment with Conditional Random Fields

• [49]

  Multilingual Deep Lexical Acquisition for HPSGs via Supertagging

  Phil Blunsom and Timothy Baldwin

  In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. Pages 164–171. Sydney‚ Australia. July, 2006.

  Details about Multilingual Deep Lexical Acquisition for HPSGs via Supertagging | BibTeX data for Multilingual Deep Lexical Acquisition for HPSGs via Supertagging

• [50]

  Question classification with log−linear models

  Phil Blunsom‚ Krystle Kocik and James R. Curran

  In SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. Pages 615–616. New York‚ NY‚ USA. 2006. ACM.

  Details about Question classification with log−linear models | BibTeX data for Question classification with log−linear models | DOI (http://doi.acm.org/10.1145/1148170.1148282)

• [51]

  Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs

  Firas Hamze and Nando De Freitas

  In Y. Weiss‚ B. Schölkopf and J. Platt, editors, Advances in Neural Information Processing Systems (NIPS). Pages 491–498. Cambridge‚ MA. 2005. MIT Press.

  Details about Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs | BibTeX data for Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs | Download (pdf) of Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs

• [52]

  Nonparametric Bayesian Logic

  Peter Carbonetto‚ Jacek Kisynski‚ Nando de Freitas and David Poole

  In Uncertainty in Artificial Intelligence (UAI). Pages 85–93. Arlington‚ Virginia. 2005. AUAI Press.

  Details about Nonparametric Bayesian Logic | BibTeX data for Nonparametric Bayesian Logic | Download (pdf) of Nonparametric Bayesian Logic

• [53]

  Learning about individuals from group statistics

  Hendrik Kuck and Nando de Freitas

  In Uncertainty in Artificial Intelligence (UAI). Pages 332–339. Arlington‚ Virginia. 2005. AUAI Press.

  Details about Learning about individuals from group statistics | BibTeX data for Learning about individuals from group statistics | Download (pdf) of Learning about individuals from group statistics

• [54]

  Fast Krylov Methods for N−Body Learning

  Nando De Freitas‚ Yang Wang‚ Maryam Mahdaviani and Dustin Lang

  In Y. Weiss‚ B. Schölkopf and J. Platt, editors, Advances in Neural Information Processing Systems (NIPS). Pages 251–258. Cambridge‚ MA. 2005. MIT Press.

  Details about Fast Krylov Methods for N−Body Learning | BibTeX data for Fast Krylov Methods for N−Body Learning | Download (pdf) of Fast Krylov Methods for N−Body Learning

• [55]

  Toward Practical N² Monte Carlo: the Marginal Particle Filter

  Mike Klaas‚ Nando de Freitas and Arnaud Doucet

  In Uncertainty in Artificial Intelligence (UAI). Pages 308–315. Arlington‚ Virginia. 2005. AUAI Press.

  Details about Toward Practical N² Monte Carlo: the Marginal Particle Filter | BibTeX data for Toward Practical N² Monte Carlo: the Marginal Particle Filter | Download (pdf) of Toward Practical N² Monte Carlo: the Marginal Particle Filter

• [56]

  Fast Computational Methods for Visually Guided Robots

  M. Mahdaviani‚ N. de Freitas‚ B. Fraser and F. Hamze

  In IEEE International Conference on Robotics & Automation (ICRA). Pages 138–143. 2005.

  Details about Fast Computational Methods for Visually Guided Robots | BibTeX data for Fast Computational Methods for Visually Guided Robots | DOI (10.1109/ROBOT.2005.1570109) | Download (pdf) of Fast Computational Methods for Visually Guided Robots

• [57]

  Maximum Entropy Markov models for semantic role labelling

  Phil Blunsom

  In Proc. of the Australasian Language Technology Workshop 2004. Pages 109–116. Sydney‚ Australia. 2005.

  Details about Maximum Entropy Markov models for semantic role labelling | BibTeX data for Maximum Entropy Markov models for semantic role labelling

• [58]

  Semantic Role Labelling with Tree Conditional Random Fields

  Trevor Cohn and Philip Blunsom

  In Proc. of the 9th Conference on Natural Language Learning (CoNLL−2005). Pages 169–172. Ann Arbor‚ Michigan. June, 2005.

  Details about Semantic Role Labelling with Tree Conditional Random Fields | BibTeX data for Semantic Role Labelling with Tree Conditional Random Fields | Link to Semantic Role Labelling with Tree Conditional Random Fields

• [59]

  From Fields to Trees

  Firas Hamze and Nando de Freitas

  In Uncertainty in Artificial Intelligence (UAI). Pages 243–250. Arlington‚ Virginia. 2004. AUAI Press.

  Details about From Fields to Trees | BibTeX data for From Fields to Trees | Download (pdf) of From Fields to Trees

• [60]

  A Boosted Particle Filter: Multitarget Detection and Tracking

  Kenji Okuma‚ Ali Taleghani‚ Nando Freitas‚ James J. Little and David G. Lowe

  In Tomas Pajdla and Jiri Matas, editors, Computer Vision − ECCV 2004. Vol. 3021 of Lecture Notes in Computer Science. Pages 28–39. Springer Berlin Heidelberg. 2004.

  Best Paper prize in Cognitive Vision

  Details about A Boosted Particle Filter: Multitarget Detection and Tracking | BibTeX data for A Boosted Particle Filter: Multitarget Detection and Tracking | DOI (10.1007/978-3-540-24670-1_3) | Link to A Boosted Particle Filter: Multitarget Detection and Tracking

• [61]

  Rao−Blackwellised Particle Filtering via Data Augmentation

  Christophe Andrieu‚ Nando de Freitas and Arnaud Doucet

  In Advances in Neural Information Processing Systems (NIPS). Pages 561–567. 2001.

  Details about Rao−Blackwellised Particle Filtering via Data Augmentation | BibTeX data for Rao−Blackwellised Particle Filtering via Data Augmentation | Download (pdf) of Rao−Blackwellised Particle Filtering via Data Augmentation

Technical reports

• [1]

  Neural Programmer−Interpreters

  Scott Reed and Nando de Freitas

  No. arXiv:1511.06279. 2015.

  Details about Neural Programmer−Interpreters | BibTeX data for Neural Programmer−Interpreters | Link to Neural Programmer−Interpreters

• [2]

  Dueling Network Architectures for Deep Reinforcement Learning

  Ziyu Wang‚ Nando de Freitas and Marc Lanctot

  No. arXiv:1511.06581. 2015.

  Details about Dueling Network Architectures for Deep Reinforcement Learning | BibTeX data for Dueling Network Architectures for Deep Reinforcement Learning | Link to Dueling Network Architectures for Deep Reinforcement Learning

• [3]

  Modelling‚ Visualising and Summarising Documents with a Single Convolutional Neural Network

  Misha Denil‚ Alban Demiraj‚ Nal Kalchbrenner‚ Phil Blunsom and Nando de Freitas

  No. arXiv:1406.3830. University of Oxford. 2014.

  Details about Modelling‚ Visualising and Summarising Documents with a Single Convolutional Neural Network | BibTeX data for Modelling‚ Visualising and Summarising Documents with a Single Convolutional Neural Network | Link to Modelling‚ Visualising and Summarising Documents with a Single Convolutional Neural Network

• [4]

  An Entropy Search Portfolio for Bayesian Optimization

  Bobak Shahriari‚ Ziyu Wang‚ Matthew W. Hoffman‚ Alexandre Bouchard−Cote and Nando de Freitas

  No. arXiv:1406.4625. University of Oxford. 2014.

  Details about An Entropy Search Portfolio for Bayesian Optimization | BibTeX data for An Entropy Search Portfolio for Bayesian Optimization | Link to An Entropy Search Portfolio for Bayesian Optimization

• [5]

  A Tutorial on Bayesian Optimization of Expensive Cost Functions‚ with Application to Active User Modeling and Hierarchical Reinforcement Learning

  Eric Brochu‚ Vlad M Cora and Nando de Freitas

  No. UBC TR−2009−023 and arXiv:1012.2599. University of British Columbia‚ Department of Computer Science. 2009.

  Details about A Tutorial on Bayesian Optimization of Expensive Cost Functions‚ with Application to Active User Modeling and Hierarchical Reinforcement Learning | BibTeX data for A Tutorial on Bayesian Optimization of Expensive Cost Functions‚ with Application to Active User Modeling and Hierarchical Reinforcement Learning | Link to A Tutorial on Bayesian Optimization of Expensive Cost Functions‚ with Application to Active User Modeling and Hierarchical Reinforcement Learning