Nando de Freitas

According to our database1, Nando de Freitas authored at least 148 papers between 2000 and 2018.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Bibliography

2018
Intrinsic Social Motivation via Causal Influence in Multi-Agent RL.
CoRR, 2018

One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL.
CoRR, 2018

Sample Efficient Adaptive Text-to-Speech.
CoRR, 2018

Large-Scale Visual Speech Recognition.
CoRR, 2018

Playing hard exploration games by watching YouTube.
CoRR, 2018

Hyperbolic Attention Networks.
CoRR, 2018

Learning Awareness Models.
CoRR, 2018

Compositional Obverter Communication Learning From Raw Visual Input.
CoRR, 2018

Reinforcement and Imitation Learning for Diverse Visuomotor Skills.
CoRR, 2018

2017
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017.
CoRR, 2017

Cortical microcircuits as gated-recurrent neural networks.
CoRR, 2017

Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions.
CoRR, 2017

Learned Optimizers that Scale and Generalize.
CoRR, 2017

Robust Imitation of Diverse Behaviors.
CoRR, 2017

Parallel Multiscale Autoregressive Density Estimation.
CoRR, 2017

Programmable Agents.
CoRR, 2017

The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously.
CoRR, 2017

Cortical microcircuits as gated-recurrent neural networks.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Robust Imitation of Diverse Behaviors.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Learned Optimizers that Scale and Generalize.
Proceedings of the 34th International Conference on Machine Learning, 2017

Parallel Multiscale Autoregressive Density Estimation.
Proceedings of the 34th International Conference on Machine Learning, 2017

Learning to Learn without Gradient Descent by Gradient Descent.
Proceedings of the 34th International Conference on Machine Learning, 2017

The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously.
Proceedings of the 1st Annual Conference on Robot Learning, CoRL 2017, Mountain View, 2017

2016
Taking the Human Out of the Loop: A Review of Bayesian Optimization.
Proceedings of the IEEE, 2016

Herded Gibbs Sampling.
Journal of Machine Learning Research, 2016

Bayesian Optimization in a Billion Dimensions via Random Embeddings.
J. Artif. Intell. Res., 2016

Sample Efficient Actor-Critic with Experience Replay.
CoRR, 2016

Learning to Communicate with Deep Multi-Agent Reinforcement Learning.
CoRR, 2016

Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks.
CoRR, 2016

Learning to Perform Physics Experiments via Deep Reinforcement Learning.
CoRR, 2016

Learning to Learn for Global Optimization of Black Box Functions.
CoRR, 2016

LipNet: Sentence-level Lipreading.
CoRR, 2016

Learning to learn by gradient descent by gradient descent.
CoRR, 2016

Learning to Communicate with Deep Multi-Agent Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Learning to learn by gradient descent by gradient descent.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Dueling Network Architectures for Deep Reinforcement Learning.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Unbounded Bayesian Optimization via Regularization.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Dueling Network Architectures for Deep Reinforcement Learning.
CoRR, 2015

Neural Programmer-Interpreters.
CoRR, 2015

ACDC: A Structured Efficient Linear Layer.
CoRR, 2015

From Group to Individual Labels Using Deep Features.
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Deep Fried Convnets.
Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

Extraction of Morphological QRS-based Biomarkers in Hypertrophic Cardiomyopathy for Risk Stratification Using L1 Regularized Logistic Regression.
Proceedings of the Computing in Cardiology, 2015

Deep Apprenticeship Learning for Playing Video Games.
Proceedings of the Learning for General Competency in Video Games, 2015

2014
Deep Fried Convnets.
CoRR, 2014

Bayesian Multi-Scale Optimistic Optimization.
CoRR, 2014

Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters.
CoRR, 2014

An Entropy Search Portfolio for Bayesian Optimization.
CoRR, 2014

Deep Multi-Instance Transfer Learning.
CoRR, 2014

A Deep Architecture for Semantic Parsing.
CoRR, 2014

Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network.
CoRR, 2014

Extraction of Salient Sentences from Labelled Documents.
CoRR, 2014

Heteroscedastic Treed Bayesian Optimisation.
CoRR, 2014

Bayesian Optimization with an Empirical Hardness Model for approximate Nearest Neighbour Search.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2014

Distributed Parameter Estimation in Probabilistic Graphical Models.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Linear and Parallel Learning of Markov Random Fields.
Proceedings of the 31th International Conference on Machine Learning, 2014

Narrowing the Gap: Random Forests In Theory and In Practice.
Proceedings of the 31th International Conference on Machine Learning, 2014

Bayesian Multi-Scale Optimistic Optimization.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013
Self-Avoiding Random Dynamics on Integer Complex Systems.
ACM Trans. Model. Comput. Simul., 2013

Best arm identification via Bayesian gap-based exploration
CoRR, 2013

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (2012)
CoRR, 2013

Herded Gibbs Sampling
CoRR, 2013

Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
CoRR, 2013

Reversible Jump MCMC Simulated Annealing for Neural Networks
CoRR, 2013

Variational MCMC
CoRR, 2013

Bayesian Optimization in a Billion Dimensions via Random Embeddings
CoRR, 2013

Efficient Learning of Practical Markov Random Fields with Exact Inference.
CoRR, 2013

Predicting Parameters in Deep Learning.
CoRR, 2013

Narrowing the Gap: Random Forests In Theory and In Practice.
CoRR, 2013

Predicting Parameters in Deep Learning.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Bayesian Optimization in High Dimensions via Random Embeddings.
Proceedings of the IJCAI 2013, 2013

Adaptive Hamiltonian and Riemann Manifold Monte Carlo.
Proceedings of the 30th International Conference on Machine Learning, 2013

Consistency of Online Random Forests.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
Learning Where to Attend with Deep Architectures for Image Tracking.
Neural Computation, 2012

Adaptive MCMC with Bayesian Optimization.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Recklessly Approximate Sparse Coding
CoRR, 2012

From Fields to Trees
CoRR, 2012

Toward Practical N2 Monte Carlo: the Marginal Particle Filter
CoRR, 2012

Learning about individuals from group statistics
CoRR, 2012

Nonparametric Bayesian Logic
CoRR, 2012

Large-Flip Importance Sampling
CoRR, 2012

New inference strategies for solving Markov Decision Processes using reversible jump MCMC
CoRR, 2012

Intracluster Moves for Constrained Discrete-Space MCMC
CoRR, 2012

Decentralized, Adaptive, Look-Ahead Particle Filtering
CoRR, 2012

Regret Bounds for Deterministic Gaussian Process Bandits
CoRR, 2012

Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood
CoRR, 2012

Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations.
Proceedings of the 29th International Conference on Machine Learning, 2012

A Machine Learning Perspective on Predictive Coding with PAQ8.
Proceedings of the 2012 Data Compression Conference, Snowbird, UT, USA, April 10-12, 2012, 2012

Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults.
Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2011
Learning where to Attend with Deep Architectures for Image Tracking
CoRR, 2011

A Machine Learning Perspective on Predictive Coding with PAQ
CoRR, 2011

Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood.
Proceedings of the UAI 2011, 2011

Portfolio Allocation for Bayesian Optimization.
Proceedings of the UAI 2011, 2011

On Autoencoders and Score Matching for Energy Based Models.
Proceedings of the 28th International Conference on Machine Learning, 2011

Learning attentional policies for tracking and recognition in video with deep networks.
Proceedings of the 28th International Conference on Machine Learning, 2011

2010
Inductive Principles for Restricted Boltzmann Machine Learning.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
CoRR, 2010

Hedging Strategies for Bayesian Optimization
CoRR, 2010

Intracluster Moves for Constrained Discrete-Space MCMC.
Proceedings of the UAI 2010, 2010

A Bayesian Interactive Optimization Approach to Procedural Animation Design.
Proceedings of the 2010 Eurographics/ACM SIGGRAPH Symposium on Computer Animation, 2010

A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets.
Proceedings of the Information Theory and Applications Workshop, 2010

2009
An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot.
Auton. Robots, 2009

New inference strategies for solving Markov Decision Processes using reversible jump MCMC.
Proceedings of the UAI 2009, 2009

Inference and Learning for Active Sensing, Experimental Design and Control.
Proceedings of the Pattern Recognition and Image Analysis, 4th Iberian Conference, 2009

2008
Learning to Recognize Objects with Little Supervision.
International Journal of Computer Vision, 2008

An interior-point stochastic approximation method and an L1-regularized delta rule.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Target-directed attention: Sequential decision-making for gaze planning.
Proceedings of the 2008 IEEE International Conference on Robotics and Automation, 2008

2007
Large-Flip Importance Sampling.
Proceedings of the UAI 2007, 2007

Preference galleries for material design.
Proceedings of the 34. International Conference on Computer Graphics and Interactive Techniques, 2007

Active Policy Learning for Robot Planning and Exploration under Uncertainty.
Proceedings of the Robotics: Science and Systems III, 2007

Bayesian Policy Learning with Trans-Dimensional MCMC.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Active Preference Learning with Discrete Choice Data.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Analysis of Particle Methods for Simultaneous Robot Localization and Mapping and a New Algorithm: Marginal-SLAM.
Proceedings of the 2007 IEEE International Conference on Robotics and Automation, 2007

2006
Conditional mean field.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Fast particle smoothing: if I had a million particles.
Proceedings of the Machine Learning, 2006

Robust Visual Tracking for Multiple Targets.
Proceedings of the Computer Vision, 2006

A Semi-supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues.
Proceedings of the Toward Category-Level Object Recognition, 2006

2005
Toward Practical N2 Monte Carlo: the Marginal Particle Filter.
Proceedings of the UAI '05, 2005

Learning about Individuals from Group Statistics.
Proceedings of the UAI '05, 2005

Nonparametric Bayesian Logic.
Proceedings of the UAI '05, 2005

Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Fast Krylov Methods for N-Body Learning.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Fast Computational Methods for Visually Guided Robots.
Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005

Fast maximum a-posteriori inference on Monte Carlo state spaces.
Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

2004
Diagnosis by a waiter and a Mars explorer.
Proceedings of the IEEE, 2004

From Fields to Trees.
Proceedings of the UAI '04, 2004

Beat Tracking the Graphical Model Way.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

A Boosted Particle Filter: Multitarget Detection and Tracking.
Proceedings of the Computer Vision, 2004

A Constrained Semi-supervised Learning Approach to Data Association.
Proceedings of the Computer Vision, 2004

A Statistical Model for General Contextual Object Recognition.
Proceedings of the Computer Vision, 2004

2003
An Introduction to MCMC for Machine Learning.
Machine Learning, 2003

Matching Words and Pictures.
Journal of Machine Learning Research, 2003

A Blessing of Dimensionality: Measure Concentration and Probabilistic Inference.
Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003

Bayesian Feature Weighting for Unsupervised Learning, with Application to Object Recognition.
Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003

The Sound of an Album Cover: A Probabilistic Approach to Multimedia.
Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003

2002
Real-Time Monitoring of Complex Industrial Processes with Particle Filters.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

"Name That Song!" A Probabilistic Approach to Querying on Music and Text.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2001
Robust Full Bayesian Learning for Radial Basis Networks.
Neural Computation, 2001

Variational MCMC.
Proceedings of the UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001

Rao-Blackwellised Particle Filtering via Data Augmentation.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

2000
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks.
Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

Reversible Jump MCMC Simulated Annealing for Neural Networks.
Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

The Unscented Particle Filter.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

Sequential Monte Carlo for model selection and estimation of neural networks.
Proceedings of the IEEE International Conference on Acoustics, 2000


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