Publications

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    (pdf)     Janice Lan, Rosanne Liu, Hattie Zhou, Jason Yosinski. LCA: Loss Change Allocation for Neural Network Training. arXiv:1909.01440. 3 September 2019. arXiv page.
    (To appear at NeurIPS 2019).
    See also: LCA talk by Jason (17 min)
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    (pdf)     Sam Greydanus, Misko Dzamba, and Jason Yosinski. Hamiltonian Neural Networks. arXiv:1906.01563 [cs.NE]. 4 June 2019. arXiv page.
    (To appear at NeurIPS 2019).
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    (pdf)     Ryan Turner, Jane Hung, Eric Frank, Yunus Saatci, and Jason Yosinski. Metropolis-Hastings Generative Adversarial Networks. International Conference on Machine Learning (ICML), 2019. 17 May 2019.
    See also: arXiv page.
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    (pdf)     Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski. Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask. arXiv:1905.01067 [cs.LG]. 3 May 2019. arXiv page.
    (To appear at NeurIPS 2019).
    First presented at the Deep Phenomena Workshop at ICML 2019 (OpenReview).
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    (pdf)     Anh Nguyen, Jason Yosinski, and Jeff Clune. Understanding Neural Networks via Feature Visualization: A survey. arXiv:1904.08939 [cs.LG]. 18 April 2019.
    arXiv page. Published as chapter in the book Explainable AI: Interpreting, Explaining and Visualizing Deep Learning.
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    (pdf)     Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, and Jason Yosinski . An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution. Advances in Neural Information Processing Systems (NeurIPS), 2018. 3 December 2018.
    See also: arXiv page.
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    (pdf)     Lionel Gueguen, Alex Sergeev, Ben Kadlec, Rosanne Liu, and Jason Yosinski. Faster Neural Networks Straight from JPEG. Advances in Neural Information Processing Systems (NeurIPS), 2018. 3 December 2018.
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    (pdf)     Chunyuan Li, Heerad Farkhoor, Rosanne Liu, and Jason Yosinski. Measuring the Intrinsic Dimension of Objective Landscapes. International Conference on Learning Representations (ICLR), 2018. 24 April 2018.
    See also: arXiv page.
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    (pdf)     Joel Lehman, Jeff Clune, Dusan Misevic, Christoph Adami, Lee Altenberg, Julie Beaulieu, Peter J. Bentley, Samuel Bernard, Guillaume Beslon, David M. Bryson, Patryk Chrabaszcz, Nick Cheney, Antoine Cully, Stephane Doncieux, Fred C. Dyer, Kai Olav Ellefsen, Robert Feldt, Stephan Fischer, Stephanie Forrest, Antoine Frénoy, Christian Gagné, Leni Le Goff, Laura M. Grabowski, Babak Hodjat, Frank Hutter, Laurent Keller, Carole Knibbe, Peter Krcah, Richard E. Lenski, Hod Lipson, Robert MacCurdy, Carlos Maestre, Risto Miikkulainen, Sara Mitri, David E. Moriarty, Jean-Baptiste Mouret, Anh Nguyen, Charles Ofria, Marc Parizeau, David Parsons, Robert T. Pennock, William F. Punch, Thomas S. Ray, Marc Schoenauer, Eric Shulte, Karl Sims, Kenneth O. Stanley, François Taddei, Danesh Tarapore, Simon Thibault, Westley Weimer, Richard Watson, and Jason Yosinski. The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities. arXiv:1803.03453 [cs.NE]. 9 March 2018.
    arXiv page.
  • first page of paper
    (pdf)     Chad DeChant, Tyr Wiesner-Hanks, Siyuan Chen, Ethan L. Stewart, Jason Yosinski, Michael A. Gore, Rebecca J. Nelson, and Hod Lipson. Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology. 24 August 2017.
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    (pdf)     Nikolay Laptev, Jason Yosinski, Li Erran Li, and Slawek Smyl. Time-Series Extreme Event Forecasting with Neural Networks at Uber. Time Series workshop at International Conference on Machine Learning (ICML), 2017. 11 August 2017.
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    (pdf)     Maithra Raghu, Justin Gilmer, Jason Yosinski, and Jascha Sohl-Dickstein. SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement. Advances in Neural Information Processing Systems (NeurIPS), 2017. 19 June 2017.
    See also: arXiv page.
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    (pdf)     Anh Nguyen, Jeff Clune, Yoshua Bengio, Alexey Dosovitskiy, and Jason Yosinski. Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 30 November 2016.
    See also: arXiv page.
    Presented as CVPR Spotlight (9.7%).
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    (pdf)     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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    (pdf)     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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    (pdf)     Sina Honari, Jason Yosinski, Pascal Vincent, and Christopher Pal. Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 27 June 2016.
    See also: arXiv page (Nov 2015), CVPR Poster.
    CVPR Spotlight (9.7%).
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    (pdf)     Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. Advances in Neural Information Processing Systems (NeurIPS), 2016. 31 May 2016.
    See also: arXiv page.
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    (pdf)     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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    (pdf)     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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    (pdf)     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%).
  • first page of paper
    (pdf)     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.
    See also: arXiv page.
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    (pdf)     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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    (pdf)     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.
    See also: arXiv page.
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    (pdf)     Guillaume Alain, Yoshua Bengio, Li Yao, Jason Yosinski, Eric Thibodeau-Laufer, Saizheng Zhang, and Pascal Vincent. GSNs: Generative Stochastic Networks. Information and Inference: A Journal of the IMA, 2016; doi: 10.1093/imaiai/iaw003. 23 March 2015.
    See also: arXiv page.
  • first page of paper
    (pdf)     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%).
  • first page of paper
    (pdf)     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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    (pdf)     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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    (pdf)     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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    (pdf)     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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    (pdf)     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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    (pdf)     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.
  • first page of paper
    (pdf)     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.
  • first page of paper
    (pdf)     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.
  • first page of paper
    (pdf)     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.
  • first page of paper
    (pdf)     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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    (pdf)     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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    (pdf)     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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    (pdf)     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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    (pdf)     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.