Analysis of CBRN sensor fusion methods. - Scott M. Lundberg; Randy C. Paffenroth; Jason Yosinski (FUSION, 2010)

Evolving robot gaits in hardware: the HyperNEAT generative encoding vs. parameter optimization. - Jason Yosinski; Jeff Clune; Diana Hidalgo; Sarah Nguyen; Juan Cristóbal Zagal; Hod Lipson (ECAL, 2011)

Generating gaits for physical quadruped robots: evolved neural networks vs. local parameterized search. - Jason Yosinski; Jeff Clune; Diana Hidalgo; Sarah Nguyen; Juan Cristóbal Zagal; Hod Lipson (GECCO (Companion), 2011)

Aracna: An Open-Source Quadruped Platform for Evolutionary Robotics. - Sara Lohmann; Jason Yosinski; Eric Gold; Jeff Clune; Jeremy Blum; Hod Lipson (ALIFE, 2012)

MAV Stabilization using Machine Learning and Onboard Sensors - Jason Yosinski; Cooper Bills (CoRR, 2012)

Evolving Gaits for Physical Robots with the HyperNEAT Generative Encoding: The Benefits of Simulation. - Suchan Lee; Jason Yosinski; Kyrre Glette; Hod Lipson; Jeff Clune (EvoApplications, 2013)

Deep Generative Stochastic Networks Trainable by Backprop. - Yoshua Bengio; Eric Thibodeau-Laufer; Jason Yosinski (CoRR, 2013)

Hands-free Evolution of 3D-printable Objects via Eye Tracking - Nick Cheney; Jeff Clune; Jason Yosinski; Hod Lipson (CoRR, 2013)

Deep Generative Stochastic Networks Trainable by Backprop. - Yoshua Bengio; Eric Laufer; Guillaume Alain; Jason Yosinski (ICML, 2014)

How transferable are features in deep neural networks? - Jason Yosinski; Jeff Clune; Yoshua Bengio; Hod Lipson (NIPS, 2014)

Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. - Anh Mai Nguyen; Jason Yosinski; Jeff Clune (CVPR, 2015)

Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning. - Anh Mai Nguyen; Jason Yosinski; Jeff Clune (GECCO, 2015)

GSNs : Generative Stochastic Networks. - Guillaume Alain; Yoshua Bengio; Li Yao; Jason Yosinski; Eric Thibodeau-Laufer; Saizheng Zhang; Pascal Vincent (CoRR, 2015)

Can deep learning help you find the perfect match? - Harm de Vries; Jason Yosinski (CoRR, 2015)

Understanding Neural Networks Through Deep Visualization. - Jason Yosinski; Jeff Clune; Anh Mai Nguyen; Thomas J. Fuchs; Hod Lipson (CoRR, 2015)

Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation. - Sina Honari; Jason Yosinski; Pascal Vincent; Christopher J. Pal (CVPR, 2016)

Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. - Anh Mai Nguyen; Alexey Dosovitskiy; Jason Yosinski; Thomas Brox; Jeff Clune (NIPS, 2016)

WebAL Comes of Age: A Review of the First 21 Years of Artificial Life on the Web. - Tim Taylor 001; Joshua Evan Auerbach; Josh C. Bongard; Jeff Clune; Simon J. Hickinbotham; Charles Ofria; Mizuki Oka; Sebastian Risi; Kenneth O. Stanley; Jason Yosinski (Artif. Life, 2016)

Convergent Learning: Do different neural networks learn the same representations? - Yixuan Li 001; Jason Yosinski; Jeff Clune; Hod Lipson; John E. Hopcroft (ICLR, 2016)

Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks. - Anh Mai Nguyen; Jason Yosinski; Jeff Clune (CoRR, 2016)

Understanding Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning. - Anh Nguyen 002; Jason Yosinski; Jeff Clune (Evol. Comput., 2016)

Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space. - Anh Nguyen 002; Jeff Clune; Yoshua Bengio; Alexey Dosovitskiy; Jason Yosinski (CVPR, 2017)

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability. - Maithra Raghu; Justin Gilmer; Jason Yosinski; Jascha Sohl-Dickstein (NIPS, 2017)

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement. - Maithra Raghu; Justin Gilmer; Jason Yosinski; Jascha Sohl-Dickstein (CoRR, 2017)

Faster Neural Networks Straight from JPEG. - Lionel Gueguen; Alex Sergeev; Rosanne Liu; Jason Yosinski (ICLR (Workshop), 2018)

Measuring the Intrinsic Dimension of Objective Landscapes. - Chunyuan Li; Heerad Farkhoor; Rosanne Liu; Jason Yosinski (ICLR (Poster), 2018)

Faster Neural Networks Straight from JPEG. - Lionel Gueguen; Alex Sergeev; Ben Kadlec; Rosanne Liu; Jason Yosinski (NeurIPS, 2018)

An intriguing failing of convolutional neural networks and the CoordConv solution. - Rosanne Liu; Joel Lehman; Piero Molino; Felipe Petroski Such; Eric Frank; Alex Sergeev; Jason Yosinski (NeurIPS, 2018)

Metropolis-Hastings Generative Adversarial Networks. - Ryan D. Turner; Jane Hung; Yunus Saatci; Jason Yosinski (CoRR, 2018)

Training and Understanding Deep Neural Networks for Robotics, Design, and Visual Perception. - Jason Yosinski (2018)

Metropolis-Hastings Generative Adversarial Networks. - Ryan D. Turner; Jane Hung; Eric Frank; Yunus Saatchi; Jason Yosinski (ICML, 2019)

Hamiltonian Neural Networks. - Samuel Greydanus; Misko Dzamba; Jason Yosinski (NeurIPS, 2019)

LCA: Loss Change Allocation for Neural Network Training. - Janice Lan; Rosanne Liu; Hattie Zhou; Jason Yosinski (NeurIPS, 2019)

Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask. - Hattie Zhou; Janice Lan; Rosanne Liu; Jason Yosinski (NeurIPS, 2019)

Understanding Neural Networks via Feature Visualization: A survey. - Anh Nguyen 002; Jason Yosinski; Jeff Clune (CoRR, 2019)

First-Order Preconditioning via Hypergradient Descent. - Ted Moskovitz; Rui Wang 052; Janice Lan; Sanyam Kapoor; Thomas Miconi; Jason Yosinski; Aditya Rawal (CoRR, 2019)

Understanding Neural Networks via Feature Visualization: A Survey. - Anh Nguyen 002; Jason Yosinski; Jeff Clune (Explainable AI, 2019)

Plug and Play Language Models: A Simple Approach to Controlled Text Generation. - Sumanth Dathathri; Andrea Madotto; Janice Lan; Jane Hung; Eric Frank; Piero Molino; Jason Yosinski; Rosanne Liu (ICLR, 2020)

Estimating Q(s,s') with Deep Deterministic Dynamics Gradients. - Ashley D. Edwards; Himanshu Sahni; Rosanne Liu; Jane Hung; Ankit Jain; Rui Wang 052; Adrien Ecoffet; Thomas Miconi; Charles Isbell; Jason Yosinski (ICML, 2020)

Supermasks in Superposition. - Mitchell Wortsman; Vivek Ramanujan; Rosanne Liu; Aniruddha Kembhavi; Mohammad Rastegari; Jason Yosinski; Ali Farhadi (NeurIPS, 2020)

The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities. - 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; et al. (Artif. Life, 2020)

When does loss-based prioritization fail? - Niel Teng Hu; Xinyu Hu; Rosanne Liu; Sara Hooker; Jason Yosinski (CoRR, 2021)

Language Models are Few-shot Multilingual Learners. - Genta Indra Winata; Andrea Madotto; Zhaojiang Lin; Rosanne Liu; Jason Yosinski; Pascale Fung (CoRR, 2021)

RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods. - 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 (CVPR Workshops, 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)