What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Jeff Clune mainly focuses on Artificial intelligence, Artificial neural network, Machine learning, Evolutionary algorithm and Convolutional neural network.
Jeff Clune combines subjects such as State and Pattern recognition with his study of Artificial intelligence.
His Pattern recognition study incorporates themes from Gradient descent and Deep neural networks.
His study looks at the relationship between Artificial neural network and fields such as Robot, as well as how they intersect with chemical problems.
His research integrates issues of Evolutionary computation, HyperNEAT and Encoding in his study of Evolutionary algorithm.
Jeff Clune undertakes multidisciplinary studies into Convolutional neural network and Layer in his work.
His most cited work include:
- How transferable are features in deep neural networks (2531 citations)
- Deep neural networks are easily fooled: High confidence predictions for unrecognizable images (1418 citations)
- How transferable are features in deep neural networks (1281 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Artificial neural network, Machine learning, Reinforcement learning and Evolutionary algorithm.
Jeff Clune incorporates Artificial intelligence and Modularity in his research.
His study explores the link between Artificial neural network and topics such as Feature that cross with problems in Set.
Within one scientific family, he focuses on topics pertaining to Visualization under Machine learning, and may sometimes address concerns connected to Probabilistic logic.
Jeff Clune has included themes like Backpropagation, Robotics, Stochastic gradient descent and Forgetting in his Reinforcement learning study.
While working on this project, Jeff Clune studies both Convolutional neural network and Layer.
He most often published in these fields:
- Artificial intelligence (75.84%)
- Artificial neural network (42.95%)
- Machine learning (27.52%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (75.84%)
- Artificial neural network (42.95%)
- Reinforcement learning (26.85%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Artificial neural network, Reinforcement learning, Function and Forgetting.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Computer vision.
Jeff Clune has researched Machine learning in several fields, including Contextual image classification, Algorithm and Camera trap.
His Artificial neural network research incorporates elements of Robot, Task, Hybrid algorithm and Parameterized complexity.
His biological study spans a wide range of topics, including Data science, Computation and Human–computer interaction.
His work carried out in the field of Computation brings together such families of science as Variety and State.
Between 2019 and 2021, his most popular works were:
- Learning to Continually Learn (31 citations)
- The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities (24 citations)
- Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Reinforcement learning, Function, Forgetting, Simple and Simplicity are his primary areas of study.
The various areas that Jeff Clune examines in his Reinforcement learning study include Range, Human–computer interaction and Heuristic.
A majority of his Function research is a blend of other scientific areas, such as Sequence learning, Lifelong learning, Artificial intelligence and Artificial neural network.
His Simple study frequently draws connections between adjacent fields such as Data science.
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.
