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2020 – today
- 2023
[i28]Maciej Sypetkowski, Morteza Rezanejad, Saber Saberian, Oren Kraus, John Urbanik, James Taylor, Ben Mabey, Mason Victors, Jason Yosinski, Alborz Rezazadeh Sereshkeh, Imran S. Haque, Berton Earnshaw:
RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods. CoRR abs/2301.05768 (2023)- 2021
- [i27]Niel Teng Hu, Xinyu Hu, Rosanne Liu, Sara Hooker, Jason Yosinski:
When does loss-based prioritization fail? CoRR abs/2107.07741 (2021) - [i26]Genta Indra Winata, Andrea Madotto, Zhaojiang Lin, Rosanne Liu, Jason Yosinski, Pascale Fung:
Language Models are Few-shot Multilingual Learners. CoRR abs/2109.07684 (2021) - 2020
- [j3]Joel Lehman, Jeff Clune, Dusan Misevic
, Christoph Adami, Lee Altenberg, Julie Beaulieu, Peter J. Bentley, Samuel Bernard, Guillaume Beslon, David M. Bryson, Nick Cheney, Patryk Chrabaszcz, Antoine Cully, Stéphane Doncieux, Fred C. Dyer, Kai Olav Ellefsen, Robert Feldt, Stephan Fischer, Stephanie Forrest, Antoine Frénoy, Christian Gagné, Léni K. 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 P. Parsons, Robert T. Pennock, William F. Punch, Thomas S. Ray, Marc Schoenauer, Eric Schulte, Karl Sims, Kenneth O. Stanley, François Taddei, Danesh Tarapore, Simon Thibault, Richard A. Watson, Westley Weimer, Jason Yosinski:
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities. Artif. Life 26(2): 274-306 (2020) - [c26]Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, Rosanne Liu:
Plug and Play Language Models: A Simple Approach to Controlled Text Generation. ICLR 2020 - [c25]Ashley D. Edwards, Himanshu Sahni, Rosanne Liu, Jane Hung, Ankit Jain, Rui Wang, Adrien Ecoffet, Thomas Miconi, Charles Isbell, Jason Yosinski:
Estimating Q(s,s') with Deep Deterministic Dynamics Gradients. ICML 2020: 2825-2835 - [c24]Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, Ali Farhadi:
Supermasks in Superposition. NeurIPS 2020 - [i25]Ashley D. Edwards, Himanshu Sahni, Rosanne Liu, Jane Hung, Ankit Jain, Rui Wang, Adrien Ecoffet, Thomas Miconi, Charles Isbell, Jason Yosinski:
Estimating Q(s, s') with Deep Deterministic Dynamics Gradients. CoRR abs/2002.09505 (2020) - [i24]Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, Ali Farhadi:
Supermasks in Superposition. CoRR abs/2006.14769 (2020)
2010 – 2019
- 2019
- [c23]Ryan D. Turner, Jane Hung, Eric Frank, Yunus Saatchi, Jason Yosinski:
Metropolis-Hastings Generative Adversarial Networks. ICML 2019: 6345-6353 - [c22]Hattie Zhou, Janice Lan, Rosanne Liu, Jason Yosinski:
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask. NeurIPS 2019: 3592-3602 - [c21]Janice Lan, Rosanne Liu, Hattie Zhou, Jason Yosinski:
LCA: Loss Change Allocation for Neural Network Training. NeurIPS 2019: 3614-3624 - [c20]Samuel Greydanus, Misko Dzamba, Jason Yosinski:
Hamiltonian Neural Networks. NeurIPS 2019: 15353-15363 - [p1]Anh Nguyen, Jason Yosinski, Jeff Clune:
Understanding Neural Networks via Feature Visualization: A Survey. Explainable AI 2019: 55-76 - [i23]Anh Nguyen, Jason Yosinski, Jeff Clune:
Understanding Neural Networks via Feature Visualization: A survey. CoRR abs/1904.08939 (2019) - [i22]Hattie Zhou, Janice Lan, Rosanne Liu, Jason Yosinski:
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask. CoRR abs/1905.01067 (2019) - [i21]Sam Greydanus, Misko Dzamba, Jason Yosinski:
Hamiltonian Neural Networks. CoRR abs/1906.01563 (2019) - [i20]Janice Lan, Rosanne Liu, Hattie Zhou, Jason Yosinski:
LCA: Loss Change Allocation for Neural Network Training. CoRR abs/1909.01440 (2019) - [i19]Ted Moskovitz, Rui Wang, Janice Lan, Sanyam Kapoor, Thomas Miconi, Jason Yosinski, Aditya Rawal:
First-Order Preconditioning via Hypergradient Descent. CoRR abs/1910.08461 (2019) - [i18]Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, Rosanne Liu:
Plug and Play Language Models: A Simple Approach to Controlled Text Generation. CoRR abs/1912.02164 (2019) - 2018
- [b1]Jason Yosinski:
Training and Understanding Deep Neural Networks for Robotics, Design, and Visual Perception. Cornell University, USA, 2018 - [c19]Lionel Gueguen, Alex Sergeev, Rosanne Liu, Jason Yosinski:
Faster Neural Networks Straight from JPEG. ICLR (Workshop) 2018 - [c18]Chunyuan Li, Heerad Farkhoor, Rosanne Liu, Jason Yosinski:
Measuring the Intrinsic Dimension of Objective Landscapes. ICLR (Poster) 2018 - [c17]Lionel Gueguen, Alex Sergeev, Ben Kadlec, Rosanne Liu, Jason Yosinski:
Faster Neural Networks Straight from JPEG. NeurIPS 2018: 3937-3948 - [c16]Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, Jason Yosinski:
An intriguing failing of convolutional neural networks and the CoordConv solution. NeurIPS 2018: 9628-9639 - [i17]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, Stéphane Doncieux, Fred C. Dyer, Kai Olav Ellefsen, Robert Feldt, Stephan Fischer, Stephanie Forrest, Antoine Frénoy, Christian Gagné, Leni K. 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 P. Parsons, Robert T. Pennock, William F. Punch, Thomas S. Ray, Marc Schoenauer, Eric Schulte, Karl Sims, Kenneth O. Stanley, François Taddei, Danesh Tarapore, Simon Thibault, Westley Weimer, Richard A. Watson, Jason Yosinski:
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities. CoRR abs/1803.03453 (2018) - [i16]Chunyuan Li, Heerad Farkhoor, Rosanne Liu, Jason Yosinski:
Measuring the Intrinsic Dimension of Objective Landscapes. CoRR abs/1804.08838 (2018) - [i15]Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, Jason Yosinski:
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution. CoRR abs/1807.03247 (2018) - [i14]Ryan D. Turner, Jane Hung, Yunus Saatci, Jason Yosinski:
Metropolis-Hastings Generative Adversarial Networks. CoRR abs/1811.11357 (2018) - 2017
- [c15]Anh Nguyen, Jeff Clune, Yoshua Bengio, Alexey Dosovitskiy, Jason Yosinski:
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space. CVPR 2017: 3510-3520 - [c14]Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein:
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability. NIPS 2017: 6076-6085 - [i13]Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein:
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement. CoRR abs/1706.05806 (2017) - 2016
- [j2]Tim Taylor, Joshua Evan Auerbach, Josh C. Bongard, Jeff Clune, Simon J. Hickinbotham, Charles Ofria, Mizuki Oka, Sebastian Risi, Kenneth O. Stanley, Jason Yosinski:
WebAL Comes of Age: A Review of the First 21 Years of Artificial Life on the Web. Artif. Life 22(3): 364-407 (2016) - [j1]Anh Nguyen, Jason Yosinski, Jeff Clune:
Understanding Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning. Evol. Comput. 24(3): 545-572 (2016) - [c13]Sina Honari, Jason Yosinski, Pascal Vincent, Christopher J. Pal:
Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation. CVPR 2016: 5743-5752 - [c12]Anh Mai Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, Jeff Clune:
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. NIPS 2016: 3387-3395 - [c11]Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, John E. Hopcroft:
Convergent Learning: Do different neural networks learn the same representations? ICLR 2016 - [i12]Anh Mai Nguyen, Jason Yosinski, Jeff Clune:
Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks. CoRR abs/1602.03616 (2016) - [i11]Anh Mai Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, Jeff Clune:
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. CoRR abs/1605.09304 (2016) - [i10]Anh Nguyen, Jason Yosinski, Yoshua Bengio, Alexey Dosovitskiy, Jeff Clune:
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space. CoRR abs/1612.00005 (2016) - 2015
- [c10]Anh Mai Nguyen, Jason Yosinski, Jeff Clune:
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. CVPR 2015: 427-436 - [c9]Anh Mai Nguyen, Jason Yosinski, Jeff Clune:
Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning. GECCO 2015: 959-966 - [c8]Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, John E. Hopcroft:
Convergent Learning: Do different neural networks learn the same representations? FE@NIPS 2015: 196-212 - [i9]Guillaume Alain, Yoshua Bengio, Li Yao, Jason Yosinski, Eric Thibodeau-Laufer, Saizheng Zhang, Pascal Vincent:
GSNs : Generative Stochastic Networks. CoRR abs/1503.05571 (2015) - [i8]Harm de Vries, Jason Yosinski:
Can deep learning help you find the perfect match? CoRR abs/1505.00359 (2015) - [i7]Jason Yosinski, Jeff Clune, Anh Mai Nguyen, Thomas J. Fuchs, Hod Lipson:
Understanding Neural Networks Through Deep Visualization. CoRR abs/1506.06579 (2015) - [i6]Sina Honari, Jason Yosinski, Pascal Vincent, Christopher J. Pal:
Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation. CoRR abs/1511.07356 (2015) - 2014
- [c7]Yoshua Bengio, Eric Laufer, Guillaume Alain, Jason Yosinski:
Deep Generative Stochastic Networks Trainable by Backprop. ICML 2014: 226-234 - [c6]Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson:
How transferable are features in deep neural networks? NIPS 2014: 3320-3328 - [i5]Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson:
How transferable are features in deep neural networks? CoRR abs/1411.1792 (2014) - [i4]Anh Mai Nguyen, Jason Yosinski, Jeff Clune:
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images. CoRR abs/1412.1897 (2014) - 2013
- [c5]Suchan Lee, Jason Yosinski, Kyrre Glette, Hod Lipson, Jeff Clune:
Evolving Gaits for Physical Robots with the HyperNEAT Generative Encoding: The Benefits of Simulation. EvoApplications 2013: 540-549 - [i3]Nick Cheney, Jeff Clune, Jason Yosinski, Hod Lipson:
Hands-free Evolution of 3D-printable Objects via Eye Tracking. CoRR abs/1304.4889 (2013) - [i2]Yoshua Bengio, Eric Thibodeau-Laufer, Jason Yosinski:
Deep Generative Stochastic Networks Trainable by Backprop. CoRR abs/1306.1091 (2013) - 2012
- [c4]Sara Lohmann, Jason Yosinski, Eric Gold, Jeff Clune, Jeremy Blum, Hod Lipson:
Aracna: An Open-Source Quadruped Platform for Evolutionary Robotics. ALIFE 2012 - [i1]Jason Yosinski, Cooper Bills:
MAV Stabilization using Machine Learning and Onboard Sensors. CoRR abs/1202.4465 (2012) - 2011
- [c3]Jason Yosinski, Jeff Clune, Diana Hidalgo, Sarah Nguyen, Juan Cristóbal Zagal, Hod Lipson:
Evolving robot gaits in hardware: the HyperNEAT generative encoding vs. parameter optimization. ECAL 2011: 890-897 - [c2]Jason Yosinski, Jeff Clune, Diana Hidalgo, Sarah Nguyen, Juan Cristóbal Zagal, Hod Lipson:
Generating gaits for physical quadruped robots: evolved neural networks vs. local parameterized search. GECCO (Companion) 2011: 31-32 - 2010
- [c1]Scott M. Lundberg, Randy C. Paffenroth, Jason Yosinski:
Analysis of CBRN sensor fusion methods. FUSION 2010: 1-8
Coauthor Index
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last updated on 2023-03-26 01:17 CET by the dblp team
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