Nando de Freitas : Publications

A Bayesian exploration−exploitation approach for optimal online sensing and planning with a visually guided mobile robot

Ruben Martinez−Cantin‚ Nando Freitas‚ Eric Brochu‚ José Castellanos and Arnaud Doucet

In Autonomous Robots. Vol. 27. No. 2. Pages 93–103. 2009.

Details about A Bayesian exploration−exploitation approach for optimal online sensing and planning with a visually guided mobile robot | BibTeX data for A Bayesian exploration−exploitation approach for optimal online sensing and planning with a visually guided mobile robot | DOI (10.1007/s10514-009-9130-2) | Link to A Bayesian exploration−exploitation approach for optimal online sensing and planning with a visually guided mobile robot

A Blessing of Dimensionality: Measure Concentration and Probabilistic Inference

Pinar Muyan and Nando de Freitas

In Artificial Intelligence and Statistics (AISTATS). 2003.

Details about A Blessing of Dimensionality: Measure Concentration and Probabilistic Inference | BibTeX data for A Blessing of Dimensionality: Measure Concentration and Probabilistic Inference | Link to A Blessing of Dimensionality: Measure Concentration and Probabilistic Inference

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

A Constrained Semi−supervised Learning Approach to Data Association

Hendrik Kueck‚ Peter Carbonetto and Nando Freitas

In Tomas Pajdla and Jiri Matas, editors, Computer Vision − ECCV 2004. Vol. 3023 of Lecture Notes in Computer Science. Pages 1–12. Springer Berlin Heidelberg. 2004.

Details about A Constrained Semi−supervised Learning Approach to Data Association | BibTeX data for A Constrained Semi−supervised Learning Approach to Data Association | DOI (10.1007/978-3-540-24672-5_1) | Link to A Constrained Semi−supervised Learning Approach to Data Association

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

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

A Semi−supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues

Peter Carbonetto‚ Gyuri Dorkó‚ Cordelia Schmid‚ Hendrik Kück and Nando Freitas

In Jean Ponce‚ Martial Hebert‚ Cordelia Schmid and Andrew Zisserman, editors, Toward Category−Level Object Recognition. Vol. 4170 of Lecture Notes in Computer Science. Pages 277−300. Springer Berlin Heidelberg. 2006.

Details about A Semi−supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues | BibTeX data for A Semi−supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues | DOI (10.1007/11957959_15) | Link to A Semi−supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues

A Statistical Model for General Contextual Object Recognition

Peter Carbonetto‚ Nando Freitas and Kobus Barnard

In Tomas Pajdla and Jiri Matas, editors, European Conference on Computer Vision (ECCV). Vol. 3021 of Lecture Notes in Computer Science. Pages 350–362. Springer Berlin Heidelberg. 2004.

Details about A Statistical Model for General Contextual Object Recognition | BibTeX data for A Statistical Model for General Contextual Object Recognition | DOI (10.1007/978-3-540-24670-1_27) | Link to A Statistical Model for General Contextual Object Recognition

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

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

A Bayesian interactive optimization approach to procedural animation design

Eric Brochu‚ Tyson Brochu and Nando de Freitas

In Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation. Pages 103–112. Aire−la−Ville‚ Switzerland‚ Switzerland. 2010. Eurographics Association.

Details about A Bayesian interactive optimization approach to procedural animation design | BibTeX data for A Bayesian interactive optimization approach to procedural animation design | Link to A Bayesian interactive optimization approach to procedural animation design

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

Active Policy Learning for Robot Planning and Exploration under Uncertainty

Ruben Martinez−Cantin‚ Nando de Freitas‚ Arnaud Doucet and Jose Castellanos

In Proceedings of Robotics: Science and Systems. Atlanta‚ GA‚ USA. June, 2007.

Details about Active Policy Learning for Robot Planning and Exploration under Uncertainty | BibTeX data for Active Policy Learning for Robot Planning and Exploration under Uncertainty | Link to Active Policy Learning for Robot Planning and Exploration under Uncertainty

Active Preference Learning with Discrete Choice Data

Brochu Eric‚ Nando de Freitas and Abhijeet Ghosh

In J.C. Platt‚ D. Koller‚ Y. Singer and S. Roweis, editors, Advances in Neural Information Processing Systems 20. Pages 409–416. MIT Press, Cambridge‚ MA. 2007.

Details about Active Preference Learning with Discrete Choice Data | BibTeX data for Active Preference Learning with Discrete Choice Data | Download (pdf) of Active Preference Learning with Discrete Choice Data

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

Adaptive MCMC with Bayesian Optimization

Nimalan Mahendran‚ Ziyu Wang‚ Firas Hamze and Nando de Freitas

In Journal of Machine Learning Research − Proceedings Track for Artificial Intelligence and Statistics (AISTATS). Vol. 22. Pages 751–760. 2012.

Details about Adaptive MCMC with Bayesian Optimization | BibTeX data for Adaptive MCMC with Bayesian Optimization | Link to Adaptive MCMC with Bayesian Optimization

An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward

Matthew Hoffman‚ Nando de Freitas‚ Arnaud Doucet and Jan Peters

In Journal of Machine Learning Research − Proceedings Track for Artificial Intelligence and Statistics (AISTATS). Vol. 5. Pages 232–239. 2009.

Details about An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward | BibTeX data for An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward | Link to An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward

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

An interior−point stochastic approximation method and an L1−regularized delta rule

Peter Carbonetto‚ Mark Schmidt and Nando de Freitas

In D. Koller‚ D. Schuurmans‚ Y. Bengio and L. Bottou, editors, Advances in Neural Information Processing Systems (NIPS). Pages 233–240. 2008.

Details about An interior−point stochastic approximation method and an L1−regularized delta rule | BibTeX data for An interior−point stochastic approximation method and an L1−regularized delta rule

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

Bayesian Analysis of Continuous Time Markov Chains with Application to Phylogenetic Modelling

Tingting Zhao‚ Ziyu Wang‚ Alexander Cumberworth‚ Joerg Gsponer‚ Nando de Freitas and Alexandre Bouchard−Côté

In Bayesian Analysis. 2015.

Details about Bayesian Analysis of Continuous Time Markov Chains with Application to Phylogenetic Modelling | BibTeX data for Bayesian Analysis of Continuous Time Markov Chains with Application to Phylogenetic Modelling | DOI (10.1214/15-BA982) | Link to Bayesian Analysis of Continuous Time Markov Chains with Application to Phylogenetic Modelling

Bayesian Feature Weighting for Unsupervised Learning‚ with Application to Object Recognition

Peter Carbonetto‚ Nando de Freitas‚ Paul Gustafson and Natalie Thompson

In Artificial Intelligence and Statistics (AISTATS). 2003.

Details about Bayesian Feature Weighting for Unsupervised Learning‚ with Application to Object Recognition | BibTeX data for Bayesian Feature Weighting for Unsupervised Learning‚ with Application to Object Recognition | Download (pdf) of Bayesian Feature Weighting for Unsupervised Learning‚ with Application to Object Recognition

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

Bayesian Optimization in High Dimensions via Random Embeddings

Ziyu Wang‚ Masrour Zoghi‚ Frank Hutter‚ David Matheson and Nando de Freitas

In International Joint Conferences on Artificial Intelligence (IJCAI) − Distinguished Paper Award. 2013.

Details about Bayesian Optimization in High Dimensions via Random Embeddings | BibTeX data for Bayesian Optimization in High Dimensions via Random Embeddings | Download (pdf) of Bayesian Optimization in High Dimensions via Random Embeddings

Bayesian Optimization in a Billion Dimensions via Random Embeddings

Ziyu Wang‚ Masrour Zoghi‚ Frank Hutter‚ David Matheson and Nando de Freitas

In Journal of Artificial Intelligence Research. Vol. 55. Pages 361–387. 2016.

Details about Bayesian Optimization in a Billion Dimensions via Random Embeddings | BibTeX data for Bayesian Optimization in a Billion Dimensions via Random Embeddings | DOI (10.1613/jair.4806) | Download (pdf) of Bayesian Optimization in a Billion Dimensions via Random Embeddings

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

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

Beat Tracking the Graphical Model Way

Dustin Lang and Nando de Freitas

In Lawrence K. Saul‚ Yair Weiss and Léon Bottou, editors, Advances in Neural Information Processing Systems (NIPS). Pages 745−752. MIT Press, Cambridge‚ MA. 2004.

Details about Beat Tracking the Graphical Model Way | BibTeX data for Beat Tracking the Graphical Model Way | Download (pdf) of Beat Tracking the Graphical Model Way

Conditional mean field

Peter Carbonetto and Nando De Freitas

In B. Schölkopf‚ J. Platt and T. Hoffman, editors, Advances in Neural Information Processing Systems (NIPS). Pages 201–208. Cambridge‚ MA. 2006. MIT Press.

Details about Conditional mean field | BibTeX data for Conditional mean field | Download (pdf) of Conditional mean field

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

Deep Apprenticeship Learning for Playing Video Games

Miroslav Bogdanovic‚ Dejan Markovikj‚ Misha Denil and Nando de Freitas

In AAAI Workshop on Learning for General Competency in Video Games. 2015.

Details about Deep Apprenticeship Learning for Playing Video Games | BibTeX data for Deep Apprenticeship Learning for Playing Video Games | Download (pdf) of Deep Apprenticeship Learning for Playing Video Games

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

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

Diagnosis by a waiter and a Mars explorer

N. de Freitas‚ R. Dearden‚ Frank Hutter‚ R. Morales−Menendez‚ J. Mutch and D. Poole

In Proceedings of the IEEE. Vol. 92. No. 3. Pages 455–468. 2004.

Details about Diagnosis by a waiter and a Mars explorer | BibTeX data for Diagnosis by a waiter and a Mars explorer | DOI (10.1109/JPROC.2003.823157) | Download (pdf) of Diagnosis by a waiter and a Mars explorer

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

Dynamic Learning with the EM Algorithm for Neural Networks

De Freitas‚ Nando‚ M. Niranjan and A. H. Gee

In Journal of VLSI Signal Processing Systems. Vol. 26. No. 1/2. Pages 119–131. 2000.

Details about Dynamic Learning with the EM Algorithm for Neural Networks | BibTeX data for Dynamic Learning with the EM Algorithm for Neural Networks | DOI (10.1023/A:1008103718973) | Link to Dynamic Learning with the EM Algorithm for Neural Networks

Empirical Testing of Fast Kernel Density Estimation Algorithms

Dustin Lang‚ Mike Klaas and Nando de Freitas

No. UBC TR−2005−03. University of British Columbia‚ Department of Computer Science. 2005.

Details about Empirical Testing of Fast Kernel Density Estimation Algorithms | BibTeX data for Empirical Testing of Fast Kernel Density Estimation Algorithms | Download (pdf) of Empirical Testing of Fast Kernel Density Estimation Algorithms

Estimation and control of industrial processes with particle filters

R. Morales−Menendez‚ N. de Freitas and D. Poole

In American Control Conference. Vol. 1. Pages 579–584. 2003.

Details about Estimation and control of industrial processes with particle filters | BibTeX data for Estimation and control of industrial processes with particle filters | DOI (10.1109/ACC.2003.1239081) | Download (pdf) of Estimation and control of industrial processes with particle filters

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

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

Fast maximum a posteriori inference in Monte Carlo state spaces

Mike Klaas‚ Dustin Lang and Nando de Freitas

In Artificial Intelligence and Statistics (AISTATS). 2005.

Details about Fast maximum a posteriori inference in Monte Carlo state spaces | BibTeX data for Fast maximum a posteriori inference in Monte Carlo state spaces | Download (pdf) of Fast maximum a posteriori inference in Monte Carlo state spaces

Fast particle smoothing: if I had a million particles

Mike Klaas‚ Mark Briers‚ Nando de Freitas‚ Arnaud Doucet‚ Simon Maskell and Dustin Lang

In International Conference on Machine Learning (ICML). Pages 481–488. New York‚ NY‚ USA. 2006. ACM.

Details about Fast particle smoothing: if I had a million particles | BibTeX data for Fast particle smoothing: if I had a million particles | DOI (10.1145/1143844.1143905) | Link to Fast particle smoothing: if I had a million particles

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

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

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

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

Hierarchical Bayesian Models for Regularization in Sequential Learning

De Freitas‚ Nando‚ M. Niranjan and A. H. Gee

In Neural Computation. Vol. 12. No. 4. Pages 933–953. 2000.

Details about Hierarchical Bayesian Models for Regularization in Sequential Learning | BibTeX data for Hierarchical Bayesian Models for Regularization in Sequential Learning | DOI (10.1162/089976600300015655) | Link to Hierarchical Bayesian Models for Regularization in Sequential Learning

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

Inductive Principles for Restricted Boltzmann Machine Learning

Benjamin Marlin‚ Kevin Swersky‚ Bo Chen and Nando de Freitas

In Journal of Machine Learning Research − Proceedings Track for Artificial Intelligence and Statistics (AISTATS). Vol. 9. Pages 509–516. 2010.

Details about Inductive Principles for Restricted Boltzmann Machine Learning | BibTeX data for Inductive Principles for Restricted Boltzmann Machine Learning | Link to Inductive Principles for Restricted Boltzmann Machine Learning

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

Inference and Learning for Active Sensing‚ Experimental Design and Control

Hendrik Kueck‚ Matt Hoffman‚ Arnaud Doucet and Nando Freitas

In Helder Araujo‚ Ana Maria Mendonca‚ Armando J. Pinho and Maria Ines Torres, editors, Pattern Recognition and Image Analysis. Vol. 5524 of Lecture Notes in Computer Science. Pages 1–10. Springer Berlin Heidelberg. 2009.

Details about Inference and Learning for Active Sensing‚ Experimental Design and Control | BibTeX data for Inference and Learning for Active Sensing‚ Experimental Design and Control | DOI (10.1007/978-3-642-02172-5_1) | Link to Inference and Learning for Active Sensing‚ Experimental Design and Control

Information Theory Tools to Rank MCMC Algorithms on Probabilistic Graphical Models

Firas Hamze‚ Jean−Noel Rivasseau and Nando de Freitas

In Information Theory and Applications Workshop (ITA). 2006.

Details about Information Theory Tools to Rank MCMC Algorithms on Probabilistic Graphical Models | BibTeX data for Information Theory Tools to Rank MCMC Algorithms on Probabilistic Graphical Models | Download (pdf) of Information Theory Tools to Rank MCMC Algorithms on Probabilistic Graphical Models

Intracluster Moves for Constrained Discrete−Space MCMC

Firas Hamze and Nando de Freitas

In Uncertainty in Artificial Intelligence (UAI). Pages 236–243. Corvallis‚ Oregon. 2010.

Details about Intracluster Moves for Constrained Discrete−Space MCMC | BibTeX data for Intracluster Moves for Constrained Discrete−Space MCMC | Link to Intracluster Moves for Constrained Discrete−Space MCMC

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

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

Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q−Networks

Jakob N. Foerster‚ Yannis M. Assael‚ Nando de Freitas and Shimon Whiteson

No. arXiv:1602.02672. 2016.

Details about Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q−Networks | BibTeX data for Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q−Networks | Link to Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q−Networks

Learning to Recognize Objects with Little Supervision

Peter Carbonetto‚ Gyuri Dorkó‚ Cordelia Schmid‚ Hendrik Kück and Nando de Freitas

In International Journal of Computer Vision. Vol. 77. No. 1−3. Pages 219–237. 2008.

Details about Learning to Recognize Objects with Little Supervision | BibTeX data for Learning to Recognize Objects with Little Supervision | DOI (10.1007/s11263-007-0067-7) | Link to Learning to Recognize Objects with Little Supervision

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

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

Matching words and pictures

Kobus Barnard‚ Pinar Duygulu‚ David Forsyth‚ Nando de Freitas‚ David M. Blei and Michael I. Jordan

In Journal of Machine Learning Research. Vol. 3. Pages 1107–1135. March, 2003.

Details about Matching words and pictures | BibTeX data for Matching words and pictures | Link to Matching words and pictures

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

N−Body Games

Albert Jiang‚ Kevin Leyton−Brown and Nando de Freitas

In NIPS workshop on Game Theory‚ Machine Learning and Reasoning under Uncertainty. 2005.

Details about N−Body Games | BibTeX data for N−Body Games | Download (pdf) of N−Body Games

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

Neural Programmer−Interpreters

Scott Reed and Nando de Freitas

In International Conference on Learning Representations (ICLR). 2016.

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

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

New inference strategies for solving Markov Decision Processes using reversible jump MCMC

Matthias Hoffman‚ Hendrik Kueck‚ Nando de Freitas and Arnaud Doucet

In Uncertainty in Artificial Intelligence (UAI). Pages 223–231. Corvallis‚ Oregon. 2009.

Details about New inference strategies for solving Markov Decision Processes using reversible jump MCMC | BibTeX data for New inference strategies for solving Markov Decision Processes using reversible jump MCMC | Link to New inference strategies for solving Markov Decision Processes using reversible jump MCMC

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

Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary

P. Duygulu‚ K. Barnard‚ J.F.G. Freitas and D.A. Forsyth

In Anders Heyden‚ Gunnar Sparr‚ Mads Nielsen and Peter Johansen, editors, European Conference on Computer Vision (ECCV). Vol. 2353 of Lecture Notes in Computer Science. Pages 97−112. Springer Berlin Heidelberg. 2002.

Best Paper prize in Cognitive Vision

Details about Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary | BibTeX data for Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary | DOI (10.1007/3-540-47979-1_7) | Link to Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary

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

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

On correlation and budget constraints in model−based bandit optimization with application to automatic machine learning

Bobak Shahriari‚ Matthew Hoffman and Nando de Freitas

In Artificial Intelligence and Statistics (AISTATS). Pages 365–374. 2014.

Details about On correlation and budget constraints in model−based bandit optimization with application to automatic machine learning | BibTeX data for On correlation and budget constraints in model−based bandit optimization with application to automatic machine learning | Download (pdf) of On correlation and budget constraints in model−based bandit optimization with application to automatic machine learning

Portfolio Allocation for Bayesian Optimization

Matthew Hoffman‚ Eric Brochu and Nando de Freitas

In Uncertainty in Artificial Intelligence (UAI). Pages 327–336. Corvallis‚ Oregon. 2011.

Details about Portfolio Allocation for Bayesian Optimization | BibTeX data for Portfolio Allocation for Bayesian Optimization | Link to Portfolio Allocation for Bayesian Optimization

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

Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults

Michael A. Osborne‚ Roman Garnett‚ Kevin Swersky and Nando de Freitas

In National Conference on Artificial Intelligence (AAAI). 2012.

Details about Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults | BibTeX data for Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults | Link to Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults

Preference galleries for material design

Eric Brochu‚ Abhijeet Ghosh and Nando de Freitas

In ACM SIGGRAPH 2007 posters − Winner of the Student RC competition at SIGGRAPH.. New York‚ NY‚ USA. 2007. ACM.

Details about Preference galleries for material design | BibTeX data for Preference galleries for material design | DOI (10.1145/1280720.1280834) | Link to Preference galleries for material design

Proceedings of the Twenty−Eigth Annual Conference on Uncertainty in Artificial Intelligence (UAI−12)

Nando de Freitas and Kevin Murphy, editors

Details about Proceedings of the Twenty−Eigth Annual Conference on Uncertainty in Artificial Intelligence (UAI−12) | BibTeX data for Proceedings of the Twenty−Eigth Annual Conference on Uncertainty in Artificial Intelligence (UAI−12) | Link to Proceedings of the Twenty−Eigth Annual Conference on Uncertainty in Artificial Intelligence (UAI−12)

Rao−Blackwellised Particle Filtering for Dynamic Bayesian Networks

Arnaud Doucet‚ Nando de Freitas‚ Kevin Murphy and Stuart Russell

In Uncertainty in Artificial Intelligence (UAI). Pages 176–183. San Francisco‚ CA. 2000. Morgan Kaufmann.

Details about Rao−Blackwellised Particle Filtering for Dynamic Bayesian Networks | BibTeX data for Rao−Blackwellised Particle Filtering for Dynamic Bayesian Networks | Download (pdf) of Rao−Blackwellised Particle Filtering for Dynamic Bayesian Networks

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

Rao−Blackwellised particle filtering for fault diagnosis

Nando de Freitas

In IEEE Aerospace Conference. Vol. 4. Pages 4−1767−4−1772 vol.4. 2002.

Details about Rao−Blackwellised particle filtering for fault diagnosis | BibTeX data for Rao−Blackwellised particle filtering for fault diagnosis | DOI (10.1109/AERO.2002.1036890) | Download (pdf) of Rao−Blackwellised particle filtering for fault diagnosis

Real−Time Monitoring of Complex Industrial Processes with Particle Filters

Ruben Morales−Menendez‚ Nando de Freitas and David Poole

In Advances in Neural Information Processing Systems (NIPS). Pages 1433–1440. 2002.

Details about Real−Time Monitoring of Complex Industrial Processes with Particle Filters | BibTeX data for Real−Time Monitoring of Complex Industrial Processes with Particle Filters | Download (pdf) of Real−Time Monitoring of Complex Industrial Processes with Particle Filters

Reversible Jump MCMC Simulated Annealing for Neural Networks

Christophe Andrieu‚ Nando de Freitas and Arnaud Doucet

In Uncertainty in Artificial Intelligence (UAI). Pages 11–18. San Francisco‚ CA. 2000. Morgan Kaufmann.

Details about Reversible Jump MCMC Simulated Annealing for Neural Networks | BibTeX data for Reversible Jump MCMC Simulated Annealing for Neural Networks | Download (pdf) of Reversible Jump MCMC Simulated Annealing for Neural Networks

Robust Full Bayesian Learning for Radial Basis Networks

Christophe Andrieu‚ Nando De Freitas and Arnaud Doucet

In Neural Computation. Vol. 13. No. 10. Pages 2359–2407. 2001.

Details about Robust Full Bayesian Learning for Radial Basis Networks | BibTeX data for Robust Full Bayesian Learning for Radial Basis Networks | DOI (10.1162/089976601750541831) | Link to Robust Full Bayesian Learning for Radial Basis Networks

Robust Visual Tracking for Multiple Targets

Yizheng Cai‚ Nando Freitas and James Little

In Aleš Leonardis‚ Horst Bischof and Axel Pinz, editors, European Conference on Computer Vision (ECCV). Vol. 3954 of Lecture Notes in Computer Science. Pages 107–118. Springer Berlin Heidelberg. 2006.

Details about Robust Visual Tracking for Multiple Targets | BibTeX data for Robust Visual Tracking for Multiple Targets | DOI (10.1007/11744085_9) | Link to Robust Visual Tracking for Multiple Targets

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

Sequential MCMC for Bayesian model selection

C. Andrieu‚ Nando De Freitas and Arnaud Doucet

In IEEE Signal Processing Workshop on Higher−Order Statistics. Pages 130–134. 1999.

Details about Sequential MCMC for Bayesian model selection | BibTeX data for Sequential MCMC for Bayesian model selection | DOI (10.1109/HOST.1999.778709) | Link to Sequential MCMC for Bayesian model selection

Sequential Monte Carlo Methods to Train Neural Network Models

De Freitas‚ Nando‚ M. A. Niranjan‚ A. H. Gee and A. Doucet

In Neural Computation. Vol. 12. No. 4. Pages 955–993. 2000.

Details about Sequential Monte Carlo Methods to Train Neural Network Models | BibTeX data for Sequential Monte Carlo Methods to Train Neural Network Models | DOI (10.1162/089976600300015664) | Link to Sequential Monte Carlo Methods to Train Neural Network Models

Sequential Monte Carlo for model selection and estimation of neural networks

C. Andrieu and N. de Freitas

In IEEE International Conference on Acoustics‚ Speech‚ and Signal Processing. Vol. 6. Pages 3410−3413 vol.6. 2000.

Details about Sequential Monte Carlo for model selection and estimation of neural networks | BibTeX data for Sequential Monte Carlo for model selection and estimation of neural networks | DOI (10.1109/ICASSP.2000.860133) | Link to Sequential Monte Carlo for model selection and estimation of neural networks

Taking the Human Out of the Loop: A Review of Bayesian Optimization

Bobak Shahriari‚ Kevin Swersky‚ Ziyu Wang‚ Ryan P. Adams and Nando de Freitas

Universities of Harvard‚ Oxford‚ Toronto‚ and Google DeepMind. 2015.

Details about Taking the Human Out of the Loop: A Review of Bayesian Optimization | BibTeX data for Taking the Human Out of the Loop: A Review of Bayesian Optimization | Download (pdf) of Taking the Human Out of the Loop: A Review of Bayesian Optimization

Target−directed attention: Sequential decision−making for gaze planning

J. Vogel and N. de Freitas

In IEEE International Conference on Robotics and Automation (ICRA). Pages 2372–2379. 2008.

Details about Target−directed attention: Sequential decision−making for gaze planning | BibTeX data for Target−directed attention: Sequential decision−making for gaze planning | DOI (10.1109/ROBOT.2008.4543568)

The Sound of an Album Cover: Probabilistic Multimedia and Information Retrieval

Eric Brochu‚ Nando de Freitas and Kejie Bao

In Artificial Intelligence and Statistics (AISTATS). 2003.

Details about The Sound of an Album Cover: Probabilistic Multimedia and Information Retrieval | BibTeX data for The Sound of an Album Cover: Probabilistic Multimedia and Information Retrieval | Download (pdf) of The Sound of an Album Cover: Probabilistic Multimedia and Information Retrieval

The Unscented Particle Filter

Rudolph van der Merwe‚ Arnaud Doucet‚ Nando de Freitas and Eric A. Wan

In Advances in Neural Information Processing Systems (NIPS). Pages 584–590. 2000.

Details about The Unscented Particle Filter | BibTeX data for The Unscented Particle Filter | Download (pdf) of The Unscented Particle Filter

Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper−Parameters

Ziyu Wang and Nando de Freitas

No. arXiv:1406.7758. University of Oxford. 2014.

Details about Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper−Parameters | BibTeX data for Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper−Parameters | Link to Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper−Parameters

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

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

Variational MCMC

Nando de Freitas‚ Pedro Hojen−Sorensen‚ Michael Jordan and Stuart Russell

In Uncertainty in Artificial Intelligence (UAI). Pages 120–127. San Francisco‚ CA. 2001. Morgan Kaufmann.

Details about Variational MCMC | BibTeX data for Variational MCMC | Download (pdf) of Variational MCMC

Why can't read?: the problem of learning semantic associations in a robot environment

Peter Carbonetto and Nando de Freitas

In Proceedings of the HLT−NAACL 2003 workshop on Learning word meaning from non−linguistic data. Pages 54–61. Stroudsburg‚ PA‚ USA. 2003. Association for Computational Linguistics.

Details about Why can't read?: the problem of learning semantic associations in a robot environment | BibTeX data for Why can't read?: the problem of learning semantic associations in a robot environment | DOI (10.3115/1119212.1119220) | Link to Why can't read?: the problem of learning semantic associations in a robot environment

ACDC: A Structured Efficient Linear Layer

Marcin Moczulski‚ Misha Denil‚ Jeremy Appleyard and Nando de Freitas

In International Conference on Learning Representations (ICLR). 2016.

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

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

Distributed Parameter Estimation in Probabilistic Graphical Models

Yariv Mizrahi‚ Misha Denil and Nando de Freitas

In Advances in Neural Information Processing Systems (NIPS). 2014.

Details about Distributed Parameter Estimation in Probabilistic Graphical Models | BibTeX data for Distributed Parameter Estimation in Probabilistic Graphical Models | Link to Distributed Parameter Estimation in Probabilistic Graphical Models

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