2023 - Research.com Computer Science in United Kingdom Leader Award
Nando de Freitas spends much of his time researching Artificial intelligence, Reinforcement learning, Machine learning, Particle filter and Bayesian optimization. His research on Artificial intelligence frequently links to adjacent areas such as Matching. The concepts of his Reinforcement learning study are interwoven with issues in Domain, Function and Human–computer interaction.
His Machine learning research integrates issues from Robot and Mobile robot. A large part of his Particle filter studies is devoted to Monte Carlo localization. His Bayesian optimization research is multidisciplinary, incorporating elements of Bayesian probability and Motion planning.
His primary areas of investigation include Artificial intelligence, Machine learning, Algorithm, Reinforcement learning and Markov chain Monte Carlo. His work in Artificial intelligence addresses issues such as Pattern recognition, which are connected to fields such as Cognitive neuroscience of visual object recognition. His work in Machine learning covers topics such as Range which are related to areas like Benchmark.
His Algorithm research incorporates themes from Inference, Sampling, Graphical model, Markov chain and Particle filter. His biological study spans a wide range of topics, including Domain, State, Function, Human–computer interaction and Control. His work on Hybrid Monte Carlo and Monte Carlo integration as part of general Markov chain Monte Carlo research is frequently linked to Distribution, thereby connecting diverse disciplines of science.
Nando de Freitas focuses on Artificial intelligence, Reinforcement learning, Machine learning, Artificial neural network and Human–computer interaction. Nando de Freitas undertakes multidisciplinary investigations into Artificial intelligence and Simple in his work. His study in Reinforcement learning is interdisciplinary in nature, drawing from both Domain, State, Function, Imitation learning and Range.
His studies in Machine learning integrate themes in fields like Cloning, Meta learning, Regression and Bellman equation. Nando de Freitas has researched Artificial neural network in several fields, including Encoder, Speech recognition, Theoretical computer science and Deep learning. Nando de Freitas usually deals with Human–computer interaction and limits it to topics linked to Key and Autonomous agent.
His primary scientific interests are in Artificial intelligence, Reinforcement learning, Human–computer interaction, Artificial neural network and Machine learning. Nando de Freitas connects Artificial intelligence with Simple in his study. The Reinforcement learning study combines topics in areas such as Domain and Function.
His Human–computer interaction research incorporates themes from Variety, Control theory and Imitation learning. Nando de Freitas has included themes like Sorting, Embedding, Encoder and Affordance in his Artificial neural network study. His studies in Machine learning integrate themes in fields like Image, Handwriting, Meta learning and Shot.
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Sequential Monte Carlo methods in practice
Arnaud Doucet;Nando De Freitas;Neil Gordon;Adrian Smith.
An Introduction to Sequential Monte Carlo Methods
Arnaud Doucet;Nando de Freitas;Neil J. Gordon.
Sequential Monte Carlo Methods in Practice (2001)
An introduction to MCMC for machine learning
Christophe Andrieu;Nando De Freitas;Arnaud Doucet;Michael I. Jordan.
Machine Learning (2003)
Taking the Human Out of the Loop: A Review of Bayesian Optimization
Bobak Shahriari;Kevin Swersky;Ziyu Wang;Ryan P. Adams.
Proceedings of the IEEE (2016)
The Unscented Particle Filter
Rudolph van der Merwe;Arnaud Doucet;Nando de Freitas;Eric A. Wan.
neural information processing systems (2000)
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
Eric Brochu;Vlad M. Cora;Nando de Freitas.
arXiv: Learning (2010)
Matching words and pictures
Kobus Barnard;Pinar Duygulu;David Forsyth;Nando de Freitas.
Journal of Machine Learning Research (2003)
Dueling network architectures for deep reinforcement learning
Ziyu Wang;Tom Schaul;Matteo Hessel;Hado Van Hasselt.
international conference on machine learning (2016)
A Boosted Particle Filter: Multitarget Detection and Tracking
Kenji Okuma;Ali Taleghani;Nando de Freitas;James J. Little.
european conference on computer vision (2004)
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz;Misha Denil;Sergio Gomez;Matthew W. Hoffman.
neural information processing systems (2016)
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