Publications

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    Anh Nguyen, Jason Yosinski, Yoshua Bengio, Alexey Dosovitskiy, and Jeff Clune. Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space. arXiv cs.CV/1612.00005. 30 November 2016.
    See also: arXiv page.
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    Tim Taylor, Joshua E. Auerbach, Josh Bongard, Jeff Clune, Simon Hickinbotham, Charles Ofria, Mizuki Oka, Sebastian Risi, Kenneth O. Stanley, and Jason Yosinski. WebAL Comes of Age: A Review of the First 21 Years of Artificial Life on the Web. Artificial Life 2016 22:3, 364-407. 17 August 2016.
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    Anh Nguyen, Jason Yosinski, and Jeff Clune. Understanding Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning. Evolutionary Computation. 1 July 2016.
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    Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. arXiv cs.NE/1605.09304. 31 May 2016.
    See also: arXiv page.
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    Yixuan Li*, Jason Yosinski*, Jeff Clune, Hod Lipson, and John Hopcroft. Convergent Learning: Do different neural networks learn the same representations?. International Conference on Learning Representations (ICLR) 3 May 2016.
    See also: arXiv page, video of NIPS FE Workshop talk. (* indicates equal contribution)
    NIPS Feature Extraction Workshop oral (6.7%), ICLR oral (5.7%).
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    Anh Nguyen, Jason Yosinski, and Jeff Clune. Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks. Workshop on Visualization for Deep Learning, International Conference on Machine Learning (ICML). 11 February 2016.
    See also: arXiv page.
    ICML Workshop: oral and best paper award.
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    Sina Honari, Jason Yosinski, Pascal Vincent, and Christopher Pal. Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation. arXiv cs.CV/1511.07356. 23 November 2015.
    See also: arXiv page.
    CVPR Spotlight.
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    Anh Nguyen, Jason Yosinski, and Jeff Clune. Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). 11 July 2015.
    Best paper award (3%).
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    Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson. Understanding Neural Networks Through Deep Visualization. Deep Learning Workshop, International Conference on Machine Learning (ICML). 10 July 2015.
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    Harm de Vries and Jason Yosinski. Can deep learning help you find the perfect match?. Deep Learning Workshop, International Conference on Machine Learning (ICML). 9 July 2015.
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    Anh Nguyen, Jason Yosinski, and Jeff Clune. Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 8 June 2015. Oral presentation (3.3%), CVPR 2015 Community Top Paper Award.
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    Guillaume Alain, Yoshua Bengio, Li Yao, Jason Yosinski, Eric Thibodeau-Laufer, Saizheng Zhang, and Pascal Vincent. GSNs: Generative Stochastic Networks. Information and Inference 2016; doi: 10.1093/imaiai/iaw003. 23 March 2015.
    See also: arXiv page.
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    Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks?. Advances in Neural Information Processing Systems 27 (NIPS '14), pages 3320 - 3328. 8 December 2014.
    See also: earlier arXiv version. Oral presentation (1.2%).
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    Anh Nguyen, Jason Yosinski, and Jeff Clune. Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images. arXiv cs.CV/1412.1897. 6 November 2014.
    See also: arXiv page.
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    Yoshua Bengio, Éric Thibodeau-Laufer, Guillaume Alain, Jason Yosinski. Deep Generative Stochastic Networks Trainable by Backprop. Proceedings of the International Conference on Machine Learning. 21 June 2014.
    See also: supplemental section, earlier and later arXiv versions.
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    Nick Cheney, Jeff Clune, Jason Yosinski, and Hod Lipson. Hands-free Evolution of 3D-printable Objects via Eye Tracking. arXiv cs.NE/1304.4889. 19 April 2013.
    See also: arXiv page.
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    Sean Lee, Jason Yosinski, Kyrre Glette, Hod Lipson, and Jeff Clune. Evolving gaits for physical robots with the HyperNEAT generative encoding: the benefits of simulation. Applications of Evolutionary Computation, pages 540-549. 5 April 2013.
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    Haocheng Shen, Jason Yosinski, Petar Kormushev, Darwin G. Caldwell, and Hod Lipson. Learning Fast Quadruped Robot Gaits with the RL PoWER Spline Parameterization. Cybernetics and Information Technologies, Volume 12, Issue 3. 22 March 2013.
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    Jeff Clune, Jason Yosinski, Eugene Doan, and Hod Lipson. EndlessForms.com: Collaboratively Evolving Objects and 3D Printing Them. Proceedings of the 13th International Conference on the Synthesis and Simulation of Living Systems. 21 July 2012.
    Winner of Best Poster award.
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    Sara Lohmann, Jason Yosinski, Eric Gold, Jeff Clune, Jeremy Blum and Hod Lipson. Aracna: An Open-Source Quadruped Platform for Evolutionary Robotics. Proceedings of the 13th International Conference on the Synthesis and Simulation of Living Systems. 19 July 2012.
    Winner of Best Presentation award.
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    Jason Yosinski and Hod Lipson. Visually Debugging Restricted Boltzmann Machine Training with a 3D Example. Presented at Representation Learning Workshop, 29th International Conference on Machine Learning. 1 July 2012.
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    Jason Yosinski, Jeff Clune, Diana Hidalgo, Sarah Nguyen, Juan Cristobal Zagal, and Hod Lipson. Evolving Robot Gaits in Hardware: the HyperNEAT Generative Encoding Vs. Parameter Optimization. Proceedings of the 20th European Conference on Artificial Life, Paris, France. pp 890-897. 8 August 2011.
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    Jason Yosinski and Cooper Bills. MAV Stabilization using Machine Learning and Onboard Sensors. Technical Report CS6780, Cornell University. 10 December 2010.
    See also arXiv version.
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    Scott Lundberg, Randy Paffenroth, and Jason Yosinski. Analysis of CBRN Sensor Fusion Methods. 2010 13th Conference on Information Fusion (FUSION). 26 July 2010.
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    Jason Yosinski and Randy Paffenroth. Nonlinear Estimation for Arrays of Chemical Sensors. Signal and Data Processing of Small Targets (SPIE 2010). Orlando, Florida. Proc. SPIE Vol. 7698, 769809. 5 April 2010.
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    Scott Lundberg, Randy Paffenroth, and Jason Yosinski. Algorithms for Distributed Chemical Sensor Fusion. Signal and Data Processing of Small Targets (SPIE 2010). Orlando, Florida. Proc. SPIE Vol. 7698, 769806. 5 April 2010.
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    Jason Yosinski, Nick Coult, and Randy Paffenroth. Network-centric Angle Only Tracking. Signal and Data Processing of Small Targets (SPIE 2009). San Diego, California. Proc. SPIE Vol. 7445, 74450O. 2 August 2009.
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    Jason Yosinski and Randy Paffenroth. A Distributed Database View of Network Tracking Systems. Signal and Data Processing of Small Targets (SPIE 2008). Orlando, Florida. Proc. SPIE Vol. 6969, 696915. 16 March 2008.