Diffusion models have attained state-of-the-art performance in generating realistic objects, including when conditioning generation on class labels. Current class-conditional diffusion models, however, implicitly model the diffusion process on all classes in a flat fashion, ignoring any known relationships between classes. Class-labeled datasets, including those common in scientific domains, are rife with internal structure. To take advantage of this structure, we propose hierarchically branched diffusion models as a novel framework for class-conditional generation. Branched diffusion models explicitly leverage the inherent relationships between distinct classes in the dataset to learn the underlying diffusion process in a hierarchical manner. We highlight several advantages of branched diffusion models over the current state-of-the-art methods for class-conditional diffusion. Firstly, they can be easily extended to novel classes in a continual-learning setting at scale. Secondly, they enable more sophisticated forms of conditional generation, such as analogy-based conditional generation (i.e. transmutation). Finally, they offer a novel interpretability into the class-conditional generation process. We extensively evaluate branched diffusion models on several benchmark and large real-world scientific datasets, spanning different data modalities (images, tabular data, and graphs). In particular, we showcase the advantages of branched diffusion models on a real-world single-cell RNA-seq dataset, where our branched model leverages the intrinsic hierarchical structure between human cell types.

In this paper a method to integrate inference and machine learning is proposed. Execution of learning algorithm is defined as a complex inference rule, which generates intrinsically new knowledge. Such a solution makes the reasoning process more creative and allows to re-conceptualize agent's experiences depending on the context. Knowledge representation used in the model is based on the Logic of Plausible Reasoning (LPR). Three groups of knowledge transmutations are defined: search transmutations that are looking for the information in data, inference transmutations that are formalized as LPR proof rules, and complex ones that can use machine learning algorithms or knowledge representation change operators. All groups can be used by inference engine in a similar manner. In the paper appropriate system model and inference algorithm are proposed. Additionally, preliminary experimental results are presented.

In this paper a method to integrate inference and machine learning is proposed. Execution of learning algorithm is defined as a complex inference rule, which generates intrinsically new knowledge. Such a solution makes the reasoning process more creative and allows to re-conceptualize agent's experiences depending on the context. Knowledge representation used in the model is based on the Logic of Plausible Reasoning (LPR). Three groups of knowledge transmutations are defined: search transmutations that are looking for the information in data, inference transmutations that are formalized as LPR proof rules, and complex ones that can use machine learning algorithms or knowledge representation change operators. All groups can be used by inference engine in a similar manner. In the paper appropriate system model and inference algorithm are proposed. Additionally, preliminary experimental results are presented.