Jeff Clune
Jeff Clune
CS Assoc. Professor, U. British Columbia; CIFAR AI Chair, Vector; Senior Research Advisor, DeepMind
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Zitiert von
Zitiert von
How transferable are features in deep neural networks?
J Yosinski, J Clune, Y Bengio, H Lipson
Advances in Neural Information Processing Systems 27 (NeurIPS '14), 9, 2014
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
A Nguyen, J Yosinski, J Clune
Computer Vision and Pattern Recognition (CVPR '15), IEEE, 2015
Understanding Neural Networks Through Deep Visualization
J Yosinski, J Clune, A Nguyen, T Fuchs, H Lipson
ICML Deep Learning Workshop, 2015
Robots that can adapt like animals
A Cully, J Clune, D Tarapore, JB Mouret
Nature 521 (7553), 503-507, 2015
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
MS Norouzzadeh, A Nguyen, M Kosmala, A Swanson, M Palmer, ...
Proceedings of the National Academy of Sciences 115 (25), E5716--E5725, 2018
Plug & play generative networks: Conditional iterative generation of images in latent space
A Nguyen, J Clune, Y Bengio, A Dosovitskiy, J Yosinski
CVPR (Conference on Computer Vision and Pattern Recognition), 2016
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
F Petroski Such, V Madhavan, E Conti, J Lehman, KO Stanley, J Clune
NeurIPS Deep Reinforcement Learning Workshop, 2018
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
A Nguyen, A Dosovitskiy, J Yosinski, T Brox, J Clune
Advances in Neural Information Processing Systems (NeurIPS), 2016
Illuminating search spaces by mapping elites
JB Mouret, J Clune
arXiv preprint arXiv:1504.04909, 2015
The evolutionary origins of modularity
J Clune, JB Mouret, H Lipson
Proceedings of the Royal Society B: Biological Sciences 280 (1755), 2013
Designing neural networks through neuroevolution
KO Stanley, J Clune, J Lehman, R Miikkulainen
Nature Machine Intelligence 1 (1), 24-35, 2019
Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding
N Cheney, R MacCurdy, J Clune, H Lipson
Proceedings of the 15th annual conference on Genetic and evolutionary …, 2013
Go-Explore: a New Approach for Hard-Exploration Problems
A Ecoffet, J Huizinga, J Lehman, KO Stanley, J Clune
arXiv preprint arXiv:1901.10995, 2019
Machine learning to classify animal species in camera trap images: applications in ecology
MA Tabak, MS Norouzzadeh, DW Wolfson, SJ Sweeney, KC VerCauteren, ...
Methods in Ecology and Evolution 10 (4), 585-590, 2019
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
E Conti, V Madhavan, FP Such, J Lehman, KO Stanley, J Clune
Neural Information Processing Systems (NeurIPS), 2018
Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks
A Nguyen, J Yosinski, J Clune
ICML Workshop on Visualization for Deep Learning, 2016
First return, then explore
A Ecoffet, J Huizinga, J Lehman, KO Stanley, J Clune
Nature 590 (7847), 580-586, 2021
Convergent Learning: Do different neural networks learn the same representations?
Y Li, J Yosinski, J Clune, H Lipson, J Hopcroft
International Conference on Learning Representations (ICLR), 2016
The surprising creativity of digital evolution: A collection of anecdotes from the evolutionary computation and artificial life research communities
J Lehman, J Clune, D Misevic, C Adami, L Altenberg, J Beaulieu, ...
Artificial Life 26 (2), 274-306, 2020
Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions
R Wang, J Lehman, J Clune, KO Stanley
Proceedings of the Genetic and Evolutionary Computation Conference, 2019
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