His primary areas of study are Artificial intelligence, Machine learning, Artificial neural network, Reinforcement learning and Computer vision. His research investigates the connection between Artificial intelligence and topics such as Pattern recognition that intersect with issues in Task and Robustness. In Machine learning, he works on issues like Embedding, which are connected to Generative grammar.
His work on Continual learning and Connectionism as part of general Artificial neural network research is often related to Set and Scalability, thus linking different fields of science. The Reinforcement learning study which covers Robot learning that intersects with Robot manipulator and Unsupervised learning. His Computer vision research focuses on Discriminative model and how it connects with Mobile robot and Classifier.
Raia Hadsell mostly deals with Artificial intelligence, Reinforcement learning, Machine learning, Robot and Computer vision. His study in Mobile robot, Artificial neural network, Deep learning, Robotics and Robot learning falls under the purview of Artificial intelligence. Raia Hadsell has researched Artificial neural network in several fields, including Reachability and Pattern recognition.
The Reinforcement learning study combines topics in areas such as Key, Human–computer interaction and Code. His work in the fields of Machine learning, such as Unsupervised learning, Supervised learning, Feature learning and Active learning, intersects with other areas such as Meta learning. In general Robot, his work in Tactile sensor is often linked to Obstacle, Bridge and Sample linking many areas of study.
Raia Hadsell spends much of his time researching Reinforcement learning, Artificial intelligence, Machine learning, Task and Human–computer interaction. His Reinforcement learning research includes themes of Lagrangian relaxation, Mathematical optimization, Key and Code. His study in the fields of Deep learning, Robotics and Artificial neural network under the domain of Artificial intelligence overlaps with other disciplines such as Process and Meta learning.
He interconnects Theoretical computer science, Reachability and Message passing in the investigation of issues within Artificial neural network. His Machine learning study is mostly concerned with Supervised learning and Feature learning. His study looks at the intersection of Human–computer interaction and topics like Robot with Representation and Local optimum.
His primary areas of investigation include Reinforcement learning, Artificial intelligence, Machine learning, Task and Deep learning. His Reinforcement learning study integrates concerns from other disciplines, such as Unsupervised learning, Key and Feature learning. His Artificial intelligence study incorporates themes from Program synthesis and Theoretical computer science.
Raia Hadsell combines subjects such as Language model, Inference and Machine translation with his study of Machine learning. His biological study spans a wide range of topics, including Robotics, Leverage and Tactile sensor. His studies deal with areas such as Message passing and Reachability as well as Artificial neural network.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Learning a similarity metric discriminatively, with application to face verification
S. Chopra;R. Hadsell;Y. LeCun.
computer vision and pattern recognition (2005)
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick;Razvan Pascanu;Neil C. Rabinowitz;Joel Veness.
Proceedings of the National Academy of Sciences of the United States of America (2017)
Dimensionality Reduction by Learning an Invariant Mapping
R. Hadsell;S. Chopra;Y. LeCun.
computer vision and pattern recognition (2006)
Progressive neural networks
Neil Charles Rabinowitz;Guillaume Desjardins;Andrei-Alexandru Rusu;Koray Kavukcuoglu.
arXiv: Learning (2017)
A Tutorial on Energy-Based Learning
Yann LeCun;Sumit Chopra;Raia Hadsell;Aurelio Ranzato.
Learning long-range vision for autonomous off-road driving
Raia Hadsell;Pierre Sermanet;Jan Ben;Ayse Erkan.
Journal of Field Robotics (2009)
Learning to Navigate in Complex Environments
Piotr Mirowski;Razvan Pascanu;Fabio Viola;Hubert Soyer.
international conference on learning representations (2016)
Progress & Compress: A scalable framework for continual learning
Jonathan Schwarz;Wojciech Czarnecki;Jelena Luketina;Agnieszka Grabska-Barwinska.
international conference on machine learning (2018)
Vector-based navigation using grid-like representations in artificial agents
Andrea Banino;Caswell Barry;Benigno Uria;Charles Blundell.
Meta-Learning with Latent Embedding Optimization
Andrei A. Rusu;Dushyant Rao;Jakub Sygnowski;Oriol Vinyals.
international conference on learning representations (2018)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: