Jason Yosinski
Jason Yosinski
Uber AI Labs; Recursion Pharmaceuticals
Verified email at uber.com - Homepage
TitleCited byYear
How transferable are features in deep neural networks?
J Yosinski, J Clune, Y Bengio, H Lipson
Advances in neural information processing systems, 3320-3328, 2014
35612014
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A Nguyen, J Yosinski, J Clune
Proceedings of the IEEE conference on computer vision and pattern …, 2015
15212015
Understanding neural networks through deep visualization
J Yosinski, J Clune, A Nguyen, T Fuchs, H Lipson
ICML Deep Learning Workshop, 2015
9982015
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
A Nguyen, J Clune, Y Bengio, A Dosovitskiy, J Yosinski
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016
3322016
Deep generative stochastic networks trainable by backprop
Y Bengio, E Thibodeau-Laufer, G Alain, J Yosinski
arXiv preprint arXiv:1306.1091, 2013
3062013
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, 3387-3395, 2016
2642016
Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks
A Nguyen, J Yosinski, J Clune
arXiv preprint arXiv:1602.03616, 2016
1352016
An intriguing failing of convolutional neural networks and the coordconv solution
R Liu, J Lehman, P Molino, FP Such, E Frank, A Sergeev, J Yosinski
Advances in Neural Information Processing Systems, 9605-9616, 2018
1172018
Time-series extreme event forecasting with neural networks at uber
N Laptev, J Yosinski, LE Li, S Smyl
International Conference on Machine Learning 34, 1-5, 2017
1072017
Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
M Raghu, J Gilmer, J Yosinski, J Sohl-Dickstein
Advances in Neural Information Processing Systems, 6076-6085, 2017
1052017
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
1052016
Advances in neural information processing systems 27
J Yosinski, J Clune, Y Bengio, H Lipson, Z Ghahramani, M Welling, ...
Curran Associates, 3320-3328, 2014
822014
Recombinator networks: Learning coarse-to-fine feature aggregation
S Honari, J Yosinski, P Vincent, C Pal
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016
752016
The surprising creativity of digital evolution
J Lehman, J Clune, D Misevic, C Adami, L Altenberg, J Beaulieu, ...
Artificial Life Conference Proceedings, 55-56, 2018
742018
Evolving robot gaits in hardware: the HyperNEAT generative encoding vs. parameter optimization.
J Yosinski, J Clune, D Hidalgo, S Nguyen, JC Zagal, H Lipson
ECAL, 890-897, 2011
742011
Measuring the intrinsic dimension of objective landscapes
C Li, H Farkhoor, R Liu, J Yosinski
arXiv preprint arXiv:1804.08838, 2018
582018
Innovation engines: Automated creativity and improved stochastic optimization via deep learning
AM Nguyen, J Yosinski, J Clune
Proceedings of the 2015 Annual Conference on Genetic and Evolutionary …, 2015
582015
Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning
C DeChant, T Wiesner-Hanks, S Chen, EL Stewart, J Yosinski, MA Gore, ...
Phytopathology 107 (11), 1426-1432, 2017
412017
Evolving gaits for physical robots with the HyperNEAT generative encoding: the benefits of simulation
S Lee, J Yosinski, K Glette, H Lipson, J Clune
European Conference on the Applications of Evolutionary Computation, 540-549, 2013
382013
Understanding innovation engines: Automated creativity and improved stochastic optimization via deep learning
A Nguyen, J Yosinski, J Clune
Evolutionary computation 24 (3), 545-572, 2016
312016
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