D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 33 Citations 16,467 66 World Ranking 6728 National Ranking 3190

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

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.

His most cited work include:

  • Dimensionality Reduction by Learning an Invariant Mapping (2066 citations)
  • Learning a similarity metric discriminatively, with application to face verification (2000 citations)
  • Overcoming catastrophic forgetting in neural networks (1485 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (70.11%)
  • Reinforcement learning (45.98%)
  • Machine learning (28.74%)

What were the highlights of his more recent work (between 2018-2021)?

  • Reinforcement learning (45.98%)
  • Artificial intelligence (70.11%)
  • Machine learning (28.74%)

In recent papers he was focusing on the following fields 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.

Between 2018 and 2021, his most popular works were:

  • Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks (129 citations)
  • Continual unsupervised representation learning (55 citations)
  • From Pixels to Percepts: Highly Robust Edge Perception and Contour Following Using Deep Learning and an Optical Biomimetic Tactile Sensor (40 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

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.

Best Publications

Learning a similarity metric discriminatively, with application to face verification

S. Chopra;R. Hadsell;Y. LeCun.
computer vision and pattern recognition (2005)

2443 Citations

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)

1845 Citations

Dimensionality Reduction by Learning an Invariant Mapping

R. Hadsell;S. Chopra;Y. LeCun.
computer vision and pattern recognition (2006)

1805 Citations

Progressive neural networks

Neil Charles Rabinowitz;Guillaume Desjardins;Andrei-Alexandru Rusu;Koray Kavukcuoglu.
arXiv: Learning (2017)

1150 Citations

A Tutorial on Energy-Based Learning

Yann LeCun;Sumit Chopra;Raia Hadsell;Aurelio Ranzato.
(2006)

872 Citations

Learning long-range vision for autonomous off-road driving

Raia Hadsell;Pierre Sermanet;Jan Ben;Ayse Erkan.
Journal of Field Robotics (2009)

418 Citations

Learning to Navigate in Complex Environments

Piotr Mirowski;Razvan Pascanu;Fabio Viola;Hubert Soyer.
international conference on learning representations (2016)

403 Citations

Progress & Compress: A scalable framework for continual learning

Jonathan Schwarz;Wojciech Czarnecki;Jelena Luketina;Agnieszka Grabska-Barwinska.
international conference on machine learning (2018)

346 Citations

Vector-based navigation using grid-like representations in artificial agents

Andrea Banino;Caswell Barry;Benigno Uria;Charles Blundell.
Nature (2018)

336 Citations

Meta-Learning with Latent Embedding Optimization

Andrei A. Rusu;Dushyant Rao;Jakub Sygnowski;Oriol Vinyals.
international conference on learning representations (2018)

312 Citations

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