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Found 2 papers, 1 papers with code
Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions
While our implementation focused on the QM9 quantum-chemical dataset for a specific design task-finding molecules with a large dipole moment-our active causal learning approach, driven by intelligent sampling and interventions, holds potential for broader applications in molecular, materials design and discovery.
Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces
Generalizing from specific (Gaussian) forward processes to discrete-state processes without a variational approximation sheds light on how to interpret diffusion models, which we discuss.
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