187 citations · 287 across the 8 of their papers we have counts for
3 papers · 1 filter
Keeping it Simple: Language Models can learn Complex Molecular Distributions
Daniel Flam-Shepherd, Kevin Zhu, Alán Aspuru-Guzik
Deep generative models of molecules have grown immensely in popularity, trained on relevant datasets, these models are used to search through chemical space. The downstream utility…
Learning quantum dynamics with latent neural ODEs
Matthew Choi, Daniel Flam-Shepherd, Thi Ha Kyaw +1
The core objective of machine-assisted scientific discovery is to learn physical laws from experimental data without prior knowledge of the systems in question. In the area of quan…
Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models
Daniel Flam-Shepherd, Tony Wu, Xuemei Gu +3
Quantum physics experiments produce interesting phenomena such as interference or entanglement, which are core properties of numerous future quantum technologies. The complex relat…