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 | 513 | 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 | 323 | 2019 |
LCA: Loss Change Allocation for Neural Network Training J Lan, R Liu, H Zhou, J Yosinski arXiv preprint arXiv:1909.01440, 2019 | 45* | 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 | 18 | 2023 |
Deconstructing Lottery Tickets: Zeros H Zhou, J Lan, R Liu, J Yosinski Signs, and the Supermask 10, 2019 | 8 | 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 | 7 | 2022 |
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 |
AdsorbML: Accelerating Adsorption Energy Calculations with Machine Learning J Lan, A Palizhati, M Shuaibi, BM Wood, B Wander, A Das, M Uyttendaele, ... arXiv preprint arXiv:2211.16486, 2022 | 3 | 2022 |
The Open Catalyst Challenge 2021: Competition Report A Das, M Shuaibi, A Palizhati, S Goyal, A Grover, A Kolluru, J Lan, A Rizvi, ... NeurIPS 2021 Competitions and Demonstrations Track, 29-40, 2022 | 1 | 2022 |
AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials J Lan, A Palizhati, M Shuaibi, B Wood, B Wander, A Das, M Uyttendaele, ... | | 2023 |
Chemistry Insights for Large Pretrained GNNs K Xu, J Lan NeurIPS 2022 AI for Science: Progress and Promises, 0 | | |
Uncovering the impact of hyperparameters for global magnitude pruning J Lan, R Chin, A Baevski, AS Morcos | | |