| Language models can learn complex molecular distributions D Flam-Shepherd, K Zhu, A Aspuru-Guzik Nature Communications 13 (1), 1-10, 2022 | 138 | 2022 |
| Mapping Gaussian process priors to Bayesian neural networks D Flam-Shepherd, J Requeima, D Duvenaud NIPS Bayesian deep learning workshop 3, 2017 | 61 | 2017 |
| Neural message passing on high order paths D Flam-Shepherd, TC Wu, P Friederich, A Aspuru-Guzik Machine Learning: Science and Technology 2 (4), 045009, 2021 | 59 | 2021 |
| Graph deconvolutional generation D Flam-Shepherd, T Wu, A Aspuru-Guzik arXiv preprint arXiv:2002.07087, 2020 | 50* | 2020 |
| Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB files D Flam-Shepherd, A Aspuru-Guzik arXiv preprint arXiv:2305.05708, 2023 | 36 | 2023 |
| Learning interpretable representations of entanglement in quantum optics experiments using deep generative models D Flam-Shepherd, TC Wu, X Gu, A Cervera-Lierta, M Krenn, ... Nature Machine Intelligence, 1-11, 2022 | 29 | 2022 |
| Learning quantum dynamics with latent neural ordinary differential equations M Choi, D Flam-Shepherd, TH Kyaw, A Aspuru-Guzik Physical Review A 105 (4), 042403, 2022 | 17 | 2022 |
| Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning D Flam-Shepherd, A Zhigalin, A Aspuru-Guzik arXiv preprint arXiv:2202.00658, 2022 | 16 | 2022 |
| Characterizing and warping the function space of bayesian neural networks D Flam-Shepherd, J Requeima, D Duvenaud NeurIPS Workshop on Bayesian Deep Learning, 2018 | 12 | 2018 |
| Bayesian Variational Optimization for Combinatorial Spaces TC Wu, D Flam-Shepherd, A Aspuru-Guzik arXiv preprint arXiv:2011.02004, 2020 | 3 | 2020 |
| Atom-by-atom protein generation and beyond with language models D Flam-Shepherd, K Zhu, A Aspuru-Guzik arXiv preprint arXiv:2308.09482, 2023 | 1 | 2023 |
Alan Aspuru-GuzikProfessor of Chemistry and Computer Science, University of Toronto (starting July 1, 2018)Verified email at utoronto.ca
