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Nando de Freitas

Identifiers

  • name variant Nando de Freitas 0.60 · backfill

Papers (72)

  1. Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models cs.LG · 2024 · author #16
  2. Reinforced Self-Training (ReST) for Language Modeling cs.CL · 2023 · author #14
  3. A Generalist Agent cs.AI · 2022 · author #20
  4. Competition-Level Code Generation with AlphaCode cs.PL · 2022 · author #24
  5. Meta-learning of Sequential Strategies cs.LG · 2019 · author #22
  6. Bayesian Optimization in AlphaGo cs.LG · 2018 · author #7
  7. Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning cs.LG · 2018 · author #8
  8. One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL cs.LG · 2018 · author #11
  9. Sample Efficient Adaptive Text-to-Speech cs.LG · 2018 · author #14
  10. Large-Scale Visual Speech Recognition cs.CV · 2018 · author #15
  11. Playing hard exploration games by watching YouTube cs.LG · 2018 · author #6
  12. Hyperbolic Attention Networks cs.NE · 2018 · author #11
  13. Learning Awareness Models cs.AI · 2018 · author #9
  14. Compositional Obverter Communication Learning From Raw Visual Input cs.AI · 2018 · author #3
  15. Reinforcement and Imitation Learning for Diverse Visuomotor Skills cs.RO · 2018 · author #10
  16. Cortical microcircuits as gated-recurrent neural networks q-bio.NC · 2017 · author #4
  17. Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions cs.NE · 2017 · author #8
  18. The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously cs.AI · 2017 · author #6
  19. Robust Imitation of Diverse Behaviors cs.LG · 2017 · author #5
  20. Programmable Agents cs.AI · 2017 · author #5
  21. Learned Optimizers that Scale and Generalize cs.LG · 2017 · author #6
  22. Parallel Multiscale Autoregressive Density Estimation cs.CV · 2017 · author #7
  23. Learning to Learn without Gradient Descent by Gradient Descent stat.ML · 2016 · author #7
  24. Learning to Perform Physics Experiments via Deep Reinforcement Learning stat.ML · 2016 · author #6
  25. LipNet: End-to-End Sentence-level Lipreading cs.LG · 2016 · author #4
  26. Sample Efficient Actor-Critic with Experience Replay cs.LG · 2016 · author #7
  27. Learning to learn by gradient descent by gradient descent cs.NE · 2016 · author #8
  28. Learning to Communicate with Deep Multi-Agent Reinforcement Learning cs.AI · 2016 · author #3
  29. Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks cs.AI · 2016 · author #3
  30. Dueling Network Architectures for Deep Reinforcement Learning cs.LG · 2015 · author #6
  31. Neural Programmer-Interpreters cs.LG · 2015 · author #2
  32. ACDC: A Structured Efficient Linear Layer cs.LG · 2015 · author #4
  33. Unbounded Bayesian Optimization via Regularization stat.ML · 2015 · author #3
  34. Deep Fried Convnets cs.LG · 2014 · author #4
  35. Extraction of Salient Sentences from Labelled Documents cs.CL · 2014 · author #3
  36. Deep Multi-Instance Transfer Learning cs.LG · 2014 · author #4
  37. Heteroscedastic Treed Bayesian Optimisation cs.LG · 2014 · author #4
  38. Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters stat.ML · 2014 · author #2
  39. An Entropy Search Portfolio for Bayesian Optimization stat.ML · 2014 · author #5
  40. Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network cs.CL · 2014 · author #5
  41. Distributed Parameter Estimation in Probabilistic Graphical Models stat.ML · 2014 · author #3
  42. A Deep Architecture for Semantic Parsing cs.CL · 2014 · author #3
  43. Bayesian Multi-Scale Optimistic Optimization stat.ML · 2014 · author #4
  44. Narrowing the Gap: Random Forests In Theory and In Practice stat.ML · 2013 · author #3
  45. Linear and Parallel Learning of Markov Random Fields stat.ML · 2013 · author #3
  46. Predicting Parameters in Deep Learning cs.LG · 2013 · author #5
  47. Exploiting correlation and budget constraints in Bayesian multi-armed bandit optimization stat.ML · 2013 · author #3
  48. Adaptive Hamiltonian and Riemann Manifold Monte Carlo Samplers stat.CO · 2013 · author #3
  49. Consistency of Online Random Forests stat.ML · 2013 · author #3
  50. Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (2012) cs.AI · 2013 · author #1
  51. Herded Gibbs Sampling cs.LG · 2013 · author #3
  52. Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks cs.LG · 2013 · author #2
  53. Reversible Jump MCMC Simulated Annealing for Neural Networks cs.LG · 2013 · author #2
  54. Variational MCMC cs.LG · 2013 · author #1
  55. Bayesian Optimization in a Billion Dimensions via Random Embeddings stat.ML · 2013 · author #5
  56. Recklessly Approximate Sparse Coding cs.LG · 2012 · author #2
  57. From Fields to Trees stat.CO · 2012 · author #2
  58. Toward Practical N2 Monte Carlo: the Marginal Particle Filter stat.CO · 2012 · author #2
  59. Learning about individuals from group statistics cs.LG · 2012 · author #2
  60. Nonparametric Bayesian Logic cs.AI · 2012 · author #3
  61. Large-Flip Importance Sampling stat.CO · 2012 · author #2
  62. New inference strategies for solving Markov Decision Processes using reversible jump MCMC cs.LG · 2012 · author #3
  63. Intracluster Moves for Constrained Discrete-Space MCMC stat.CO · 2012 · author #2
  64. Decentralized, Adaptive, Look-Ahead Particle Filtering stat.ML · 2012 · author #3
  65. Regret Bounds for Deterministic Gaussian Process Bandits cs.LG · 2012 · author #1
  66. Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood cs.LG · 2012 · author #2
  67. Self-Avoiding Random Dynamics on Integer Complex Systems stat.CO · 2011 · author #3
  68. Bayesian Optimization for Adaptive MCMC stat.CO · 2011 · author #4
  69. Learning where to Attend with Deep Architectures for Image Tracking cs.AI · 2011 · author #4
  70. A Machine Learning Perspective on Predictive Coding with PAQ cs.LG · 2011 · author #2
  71. A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning cs.LG · 2010 · author #3
  72. Portfolio Allocation for Bayesian Optimization cs.LG · 2010 · author #3

Mentions

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Frequent Coauthors