Deep Learning: Publications
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[1]
"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
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[2]
A Convolutional Neural Network for Modelling Sentences
Nal Kalchbrenner‚ Edward Grefenstette and Phil Blunsom
In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. June, 2014.
Details about A Convolutional Neural Network for Modelling Sentences | BibTeX data for A Convolutional Neural Network for Modelling Sentences | Link to A Convolutional Neural Network for Modelling Sentences
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[3]
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
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[4]
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
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[5]
A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets
Kevin Swersky‚ Bo Chen‚ Ben Marlin and Nando de Freitas
In Information Theory and Applications Workshop (ITA). Pages 1−10. 2010.
Details about A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets | BibTeX data for A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets | DOI (10.1109/ITA.2010.5454138) | Link to A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets
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[6]
ACDC: A Structured Efficient Linear Layer
Marcin Moczulski‚ Misha Denil‚ Jeremy Appleyard and Nando de Freitas
No. arXiv:1511.05946. 2015.
Details about ACDC: A Structured Efficient Linear Layer | BibTeX data for ACDC: A Structured Efficient Linear Layer | Link to ACDC: A Structured Efficient Linear Layer
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[7]
Asymptotic Efficiency of Deterministic Estimators for Discrete Energy−Based Models: Ratio Matching and Pseudolikelihood
Benjamin Marlin and Nando de Freitas
In Uncertainty in Artificial Intelligence (UAI). Corvallis‚ Oregon. 2011. AUAI Press.
Details about Asymptotic Efficiency of Deterministic Estimators for Discrete Energy−Based Models: Ratio Matching and Pseudolikelihood | BibTeX data for Asymptotic Efficiency of Deterministic Estimators for Discrete Energy−Based Models: Ratio Matching and Pseudolikelihood | Link to Asymptotic Efficiency of Deterministic Estimators for Discrete Energy−Based Models: Ratio Matching and Pseudolikelihood
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[8]
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
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[9]
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
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[10]
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
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[11]
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
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[12]
Learning attentional policies for tracking and recognition in video with deep networks
Loris Bazzani‚ Nando Freitas‚ Hugo Larochelle‚ Vittorio Murino and Jo−Anne Ting
In Lise Getoor and Tobias Scheffer, editors, Proceedings of the 28th International Conference on Machine Learning (ICML−11). Pages 937–944. New York‚ NY‚ USA. June, 2011. ACM.
Details about Learning attentional policies for tracking and recognition in video with deep networks | BibTeX data for Learning attentional policies for tracking and recognition in video with deep networks | Link to Learning attentional policies for tracking and recognition in video with deep networks
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[13]
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
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[14]
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
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[15]
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
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[16]
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
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[17]
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
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[18]
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
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[19]
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
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[20]
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
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[21]
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
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[22]
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
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[23]
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