Diffusion models have demonstrated effectiveness for generating natural images and have since been adapted to generate diverse types of data, including graphs. While this emerging family of diffusion-based graph generative models has shown remarkable performance gains over predecessors that rely on variational autoencoders or generative adversarial networks, it is important to note that the majority of these models utilize Gaussian or categorical-based diffusion processes, which may encounter difficulties when modeling sparse and long-tailed data distributions. In our work, we introduce Graph Beta Diffusion (GBD), a diffusion-based generative model adept at modeling diverse graph structures. Focusing on the sparse and range-bounded characteristics of graph adjacency matrices, GBD employs a beta diffusion process to ensure that the initial distribution aligns with the beta distribution, which is well-suited for modeling such data types. To enhance the realism of generated graphs further, we introduce a modulation technique that stabilizes the generation of important graph structures while maintaining flexibility for the rest. The superior performance of GBD in generating graphs, as demonstrated across three generic graph benchmarks and two biochemical graph benchmarks, underscores its effectiveness in capturing the complexities of real-world graph data.

For downstream applications of vision-language pre-trained models, there has been significant interest in constructing effective prompts. Existing works on prompt engineering, which either require laborious manual designs or optimize the prompt tuning as a point estimation problem, may fail to describe diverse characteristics of categories and limit their applications. We introduce a Bayesian probabilistic resolution to prompt tuning, where the label-specific stochastic prompts are generated hierarchically by first sampling a latent vector from an underlying distribution and then employing a lightweight generative model. Importantly, we semantically regularize the tuning process by minimizing the statistical distance between the visual patches and linguistic prompts, which pushes the stochastic label representations to faithfully capture diverse visual concepts, instead of overfitting the training categories. We evaluate the effectiveness of our approach on four tasks: few-shot image recognition, base-to-new generalization, dataset transfer learning, and domain shifts. Extensive results over 15 datasets show promising transferability and generalization performance of our proposed model, both quantitatively and qualitatively.

Deep topic models have shown an impressive ability to extract multi-layer document latent representations and discover hierarchical semantically meaningful topics. However, most deep topic models are limited to the single-step generative process, despite the fact that the progressive generative process has achieved impressive performance in modeling image data. To this end, in this paper, we propose a novel progressive deep topic model that consists of a knowledge-informed textural data coarsening process and a corresponding progressive generative model. The former is used to build multi-level observations ranging from concrete to abstract, while the latter is used to generate more concrete observations gradually. Additionally, we incorporate a graph-enhanced decoder to capture the semantic relationships among words at different levels of observation. Furthermore, we perform a theoretical analysis of the proposed model based on the principle of information theory and show how it can alleviate the wellknown “latent variable collapse” problem. Finally, extensive experiments demonstrate that our proposed model effectively improves the ability of deep topic models, resulting in higher-quality latent document representations and topics.