J
Jeff Clune
Researcher at OpenAI
Publications - 145
Citations - 26313
Jeff Clune is an academic researcher from OpenAI. The author has contributed to research in topics: Artificial neural network & Reinforcement learning. The author has an hindex of 48, co-authored 140 publications receiving 21194 citations. Previous affiliations of Jeff Clune include University of Wyoming & University of Vermont.
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How transferable are features in deep neural networks
TL;DR: This paper quantifies the generality versus specificity of neurons in each layer of a deep convolutional neural network and reports a few surprising results, including that initializing a network with transferred features from almost any number of layers can produce a boost to generalization that lingers even after fine-tuning to the target dataset.
Proceedings Article
How transferable are features in deep neural networks
TL;DR: In this paper, the authors quantify the transferability of features from the first layer to the last layer of a deep neural network and show that transferability is negatively affected by two distinct issues: (1) the specialization of higher layer neurons to their original task at the expense of performance on the target task and (2) optimization difficulties related to splitting networks between co-adapted neurons.
Proceedings ArticleDOI
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
TL;DR: In this article, the authors show that it is possible to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99.99% confidence.
Posted Content
Understanding Neural Networks Through Deep Visualization
TL;DR: This work introduces several new regularization methods that combine to produce qualitatively clearer, more interpretable visualizations of convolutional neural networks.
Journal ArticleDOI
Robots that can adapt like animals
Antoine Cully,Jeff Clune,Danesh Tarapore,Danesh Tarapore,Danesh Tarapore,Jean-Baptiste Mouret +5 more
TL;DR: An intelligent trial-and-error algorithm is introduced that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans, and may shed light on the principles that animals use to adaptation to injury.