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The role of HG in the analysis of temporal iteration and interaural correlation
A Comprehensive Survey on Generative Diffusion Models for Structured Data
In recent years, generative diffusion models have achieved a rapid paradigm
shift in deep generative models by showing groundbreaking performance across
various applications. Meanwhile, structured data, encompassing tabular and time
series data, has been received comparatively limited attention from the deep
learning research community, despite its omnipresence and extensive
applications. Thus, there is still a lack of literature and its reviews on
structured data modelling via diffusion models, compared to other data
modalities such as visual and textual data. To address this gap, we present a
comprehensive review of recently proposed diffusion models in the field of
structured data. First, this survey provides a concise overview of the
score-based diffusion model theory, subsequently proceeding to the technical
descriptions of the majority of pioneering works that used structured data in
both data-driven general tasks and domain-specific applications. Thereafter, we
analyse and discuss the limitations and challenges shown in existing works and
suggest potential research directions. We hope this review serves as a catalyst
for the research community, promoting developments in generative diffusion
models for structured data.Comment: 20 pages, 1 figure, 2 table
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