| 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 | 791 | 2019 |
| Deconstructing lottery tickets: Zeros, signs, and the supermask H Zhou, J Lan, R Liu, J Yosinski Advances in neural information processing systems 32, 2019 | 417 | 2019 |
| The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts R Tran, J Lan, M Shuaibi, BM Wood, S Goyal, A Das, J Heras-Domingo, ... ACS Catalysis 13 (5), 3066-3084, 2023 | 71 | 2023 |
| LCA: Loss Change Allocation for Neural Network Training J Lan, R Liu, H Zhou, J Yosinski arXiv preprint arXiv:1909.01440, 2019 | 46* | 2019 |
| Spherical channels for modeling atomic interactions L Zitnick, A Das, A Kolluru, J Lan, M Shuaibi, A Sriram, Z Ulissi, B Wood Advances in Neural Information Processing Systems 35, 8054-8067, 2022 | 36 | 2022 |
| Adsorbml: Accelerating adsorption energy calculations with machine learning J Lan, A Palizhati, M Shuaibi, BM Wood, B Wander, A Das, M Uyttendaele, ... arXiv e-prints, arXiv: 2211.16486, 2022 | 13 | 2022 |
| Deconstructing Lottery Tickets: Zeros H Zhou, J Lan, R Liu, J Yosinski Signs, and the Supermask 10, 2019 | 9 | 2019 |
| AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials J Lan, A Palizhati, M Shuaibi, BM Wood, B Wander, A Das, M Uyttendaele, ... npj Computational Materials 9 (1), 172, 2023 | 5 | 2023 |
| First-order preconditioning via hypergradient descent T Moskovitz, R Wang, J Lan, S Kapoor, T Miconi, J Yosinski, A Rawal arXiv preprint arXiv:1910.08461, 2019 | 5 | 2019 |
| Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask.(May 2019) H Zhou, J Lan, R Liu, J Yosinski URL http://arxiv. org/abs, 1905 | 4 | 1905 |
| The Open Catalyst Challenge 2021: Competition Report. A Das, M Shuaibi, A Palizhati, S Goyal, A Grover, A Kolluru, J Lan, A Rizvi, ... NeurIPS (Competition and Demos), 29-40, 2021 | 2 | 2021 |
| Predictive Uncertainty Quantification for Graph Neural Network Driven Relaxed Energy Calculations J Musielewicz, J Lan, M Uyttendaele NeurIPS 2023 AI for Science Workshop, 2023 | | 2023 |
| Chemistry Insights for Large Pretrained GNNs K Xu, J Lan NeurIPS 2022 AI for Science: Progress and Promises, 2022 | | 2022 |
| Uncovering the impact of hyperparameters for global magnitude pruning J Lan, R Chin, A Baevski, AS Morcos | | 2020 |
Jason YosinskiWindscape AI; ML CollectiveVerified email at windscape.ai
