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Daniel Flam-Shepherd
Daniel Flam-Shepherd
Verified email at cs.toronto.edu - Homepage
Title
Cited by
Cited by
Year
Language models can learn complex molecular distributions
D Flam-Shepherd, K Zhu, A Aspuru-Guzik
Nature Communications 13 (1), 1-10, 2022
1012022
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
562021
Mapping Gaussian process priors to Bayesian neural networks
D Flam-Shepherd, J Requeima, D Duvenaud
NIPS Bayesian deep learning workshop 3, 2017
562017
Graph deconvolutional generation
D Flam-Shepherd, T Wu, A Aspuru-Guzik
arXiv preprint arXiv:2002.07087, 2020
47*2020
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
242022
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
162023
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
152022
Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning
D Flam-Shepherd, A Zhigalin, A Aspuru-Guzik
arXiv preprint arXiv:2202.00658, 2022
142022
Characterizing and warping the function space of bayesian neural networks
D Flam-Shepherd, J Requeima, D Duvenaud
NeurIPS Workshop on Bayesian Deep Learning, 2018
112018
Bayesian Variational Optimization for Combinatorial Spaces
TC Wu, D Flam-Shepherd, A Aspuru-Guzik
arXiv preprint arXiv:2011.02004, 2020
32020
Atom-by-atom protein generation and beyond with language models
D Flam-Shepherd, K Zhu, A Aspuru-Guzik
arXiv preprint arXiv:2308.09482, 2023
12023
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