Jason Yosinski

According to our database1, Jason Yosinski authored at least 32 papers between 2010 and 2018.

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Bibliography

2018
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution.
CoRR, 2018

Measuring the Intrinsic Dimension of Objective Landscapes.
CoRR, 2018

2017
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement.
CoRR, 2017

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016
Understanding Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning.
Evolutionary Computation, 2016

Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks.
CoRR, 2016

Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space.
CoRR, 2016

Synthesizing the preferred inputs for neurons in neural networks via deep generator networks.
CoRR, 2016

WebAL Comes of Age: A Review of the First 21 Years of Artificial Life on the Web.
Artificial Life, 2016

Synthesizing the preferred inputs for neurons in neural networks via deep generator networks.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

2015
Understanding Neural Networks Through Deep Visualization.
CoRR, 2015

Can deep learning help you find the perfect match?
CoRR, 2015

Convergent Learning: Do different neural networks learn the same representations?
CoRR, 2015

Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation.
CoRR, 2015

GSNs : Generative Stochastic Networks.
CoRR, 2015

Convergent Learning: Do different neural networks learn the same representations?
Proceedings of the 1st Workshop on Feature Extraction: Modern Questions and Challenges, 2015

Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning.
Proceedings of the Genetic and Evolutionary Computation Conference, 2015

Deep neural networks are easily fooled: High confidence predictions for unrecognizable images.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

2014
How transferable are features in deep neural networks?
CoRR, 2014

Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images.
CoRR, 2014

How transferable are features in deep neural networks?
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Deep Generative Stochastic Networks Trainable by Backprop.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Hands-free Evolution of 3D-printable Objects via Eye Tracking
CoRR, 2013

Deep Generative Stochastic Networks Trainable by Backprop.
CoRR, 2013

Evolving Gaits for Physical Robots with the HyperNEAT Generative Encoding: The Benefits of Simulation.
Proceedings of the Applications of Evolutionary Computation - 16th European Conference, 2013

2012
MAV Stabilization using Machine Learning and Onboard Sensors
CoRR, 2012

Aracna: An Open-Source Quadruped Platform for Evolutionary Robotics.
Proceedings of the Artificial Life 13: Proceedings of the Thirteenth International Conference on the Simulation and Synthesis of Living Systems, 2012

2011
Generating gaits for physical quadruped robots: evolved neural networks vs. local parameterized search.
Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, 2011

Evolving robot gaits in hardware: the HyperNEAT generative encoding vs. parameter optimization.
Proceedings of the Advances in Artificial Life: 20th Anniversary Edition, 2011

2010
Analysis of CBRN sensor fusion methods.
Proceedings of the 13th Conference on Information Fusion, 2010


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