Nando de Freitas : Publications
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[1]
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
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[2]
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
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[3]
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
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[4]
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
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[5]
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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[6]
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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[7]
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
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[8]
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
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[9]
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
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[10]
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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[11]
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
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[12]
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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[13]
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
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[14]
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
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[15]
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
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[16]
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
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[17]
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
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[18]
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
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[19]
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
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[20]
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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[21]
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
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[22]
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
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[23]
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
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[24]
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
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[25]
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
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[26]
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
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[27]
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
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[28]
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
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[29]
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
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[30]
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
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[31]
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
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[32]
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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[33]
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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[34]
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
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[35]
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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[36]
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
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[37]
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
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[38]
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
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[39]
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
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[40]
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
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[41]
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
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[42]
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
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[43]
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
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[44]
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
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[45]
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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[46]
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
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[47]
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
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[48]
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
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[49]
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
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[50]
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
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[51]
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
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[52]
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
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[53]
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
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[54]
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
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[55]
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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[56]
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
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[57]
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
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[58]
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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[59]
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
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[60]
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
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[61]
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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[62]
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
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[63]
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
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[64]
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
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[65]
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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[66]
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
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[67]
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
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[68]
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
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[69]
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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[70]
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
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[71]
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
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[72]
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
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[73]
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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[74]
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
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[75]
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
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[76]
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)
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[77]
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
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[78]
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
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[79]
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
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[80]
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
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[81]
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
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[82]
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
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[83]
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
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[84]
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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[85]
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
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[86]
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
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[87]
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
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[88]
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
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[89]
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)
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[90]
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
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[91]
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
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[92]
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
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[93]
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
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[94]
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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[95]
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
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[96]
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
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[97]
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
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[98]
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
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[99]
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
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[100]
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