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 | 10583 | 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 | 4078 | 2015 |
Understanding Neural Networks Through Deep Visualization J Yosinski, J Clune, A Nguyen, T Fuchs, H Lipson ICML Deep Learning Workshop, 2015 | 2353 | 2015 |
Robots that can adapt like animals A Cully, J Clune, D Tarapore, JB Mouret Nature 521 (7553), 503-507, 2015 | 1191 | 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 | 1039 | 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 | 971 | 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 | 893* | 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 | 827 | 2016 |
Illuminating search spaces by mapping elites JB Mouret, J Clune arXiv preprint arXiv:1504.04909, 2015 | 796 | 2015 |
The evolutionary origins of modularity J Clune, JB Mouret, H Lipson Proceedings of the Royal Society B: Biological Sciences 280 (1755), 2013 | 762 | 2013 |
Designing neural networks through neuroevolution KO Stanley, J Clune, J Lehman, R Miikkulainen Nature Machine Intelligence 1 (1), 24-35, 2019 | 730 | 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 | 452 | 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 | 431 | 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 | 429 | 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 | 409 | 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 | 394 | 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 | 372* | 2020 |
First return, then explore A Ecoffet, J Huizinga, J Lehman, KO Stanley, J Clune Nature 590 (7847), 580-586, 2021 | 360 | 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 | 348 | 2016 |
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 | 327* | 2019 |