| How transferable are features in deep neural networks? J Yosinski, J Clune, Y Bengio, H Lipson Advances in neural information processing systems 27, 2014 | 9690 | 2014 |
| 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 | 3733 | 2015 |
| Understanding neural networks through deep visualization J Yosinski, J Clune, A Nguyen, T Fuchs, H Lipson ICML Deep Learning Workshop, 2015 | 2203 | 2015 |
| 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 | 867 | 2016 |
| An intriguing failing of convolutional neural networks and the coordconv solution R Liu, J Lehman, P Molino, F Petroski Such, E Frank, A Sergeev, ... Advances in neural information processing systems 31, 2018 | 813 | 2018 |
| Hamiltonian neural networks S Greydanus, M Dzamba, J Yosinski Advances in neural information processing systems 32, 2019 | 791 | 2019 |
| 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 29, 2016 | 749 | 2016 |
| Plug and play language models: A simple approach to controlled text generation S Dathathri, A Madotto, J Lan, J Hung, E Frank, P Molino, J Yosinski, ... arXiv preprint arXiv:1912.02164, 2019 | 662 | 2019 |
| 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 30, 2017 | 585 | 2017 |
| Deep generative stochastic networks trainable by backprop Y Bengio, E Thibodeau-Laufer, G Alain, J Yosinski arXiv preprint arXiv:1306.1091, 2013 | 463 | 2013 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 444 | 2022 |
| 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 | 444 | 2017 |
| Deconstructing lottery tickets: Zeros, signs, and the supermask H Zhou, J Lan, R Liu, J Yosinski Advances in neural information processing systems 32, 2019 | 383 | 2019 |
| 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 | 356 | 2016 |
| 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 | 342 | 2017 |
| 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 | 335* | 2018 |
| 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 | 306 | 2016 |
| Measuring the intrinsic dimension of objective landscapes C Li, H Farkhoor, R Liu, J Yosinski arXiv preprint arXiv:1804.08838, 2018 | 269 | 2018 |
| Faster neural networks straight from jpeg L Gueguen, A Sergeev, B Kadlec, R Liu, J Yosinski Advances in Neural Information Processing Systems 31, 2018 | 203 | 2018 |
| Supermasks in superposition M Wortsman, V Ramanujan, R Liu, A Kembhavi, M Rastegari, J Yosinski, ... Advances in Neural Information Processing Systems 33, 15173-15184, 2020 | 197 | 2020 |
Hod LipsonProfessor of Mechanical Engineering, Columbia UniversityVerified email at columbia.edu
