A Probabilistic Framework for Modular Continual Learning
Lazar Valkov, Akash Srivastava, et al.
ICLR 2024
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly. We present the Generative Toolkit for Scientific Discovery (GT4SD). This extensible open-source library enables scientists, developers, and researchers to train and use state-of-the-art generative models to accelerate scientific discovery focused on organic material design.
Lazar Valkov, Akash Srivastava, et al.
ICLR 2024
Michael Feffer, Martin Hirzel, et al.
ICML 2022
Ehud Aharoni, Nir Drucker, et al.
CCS 2022
Bo Zhao, Iordan Ganev, et al.
ICLR 2023