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Found 4 papers, 1 papers with code
Can LLMs Generate Diverse Molecules? Towards Alignment with Structural Diversity
In response, we propose a new method for fine-tuning molecular generative LLMs to autoregressively generate a set of structurally diverse molecules, where each molecule is generated by conditioning on the previously generated molecules.
Pessimistic Backward Policy for GFlowNets
This paper studies Generative Flow Networks (GFlowNets), which learn to sample objects proportionally to a given reward function through the trajectory of state transitions.
Learning Energy Decompositions for Partial Inference of GFlowNets
In particular, we focus on improving GFlowNet with partial inference: training flow functions with the evaluation of the intermediate states or transitions.
Diffusion Probabilistic Models for Structured Node Classification
This paper studies structured node classification on graphs, where the predictions should consider dependencies between the node labels.
