Joint Generative

Joint generative modeling focuses on creating models that simultaneously generate multiple related data modalities, aiming for coherent and aligned outputs across these modalities. Current research emphasizes the use of diffusion models, variational autoencoders, and transformers, often incorporating techniques like cross-modal conditioning and Markov Chain Monte Carlo inference to improve alignment and expressiveness. This field is significant for its applications in diverse areas, including multimodal data imputation, de novo drug design, and the generation of complex systems like scene graphs and macromolecular complexes, ultimately advancing our ability to model and understand intricate relationships within and between different data types.