Learning for Structured Data
Structured data are an effective representation for complex information, providing a straightforward means to bridge numerical data and symbolic relationships. The tutorial will provide an easy paced introduction to the lively field of deep learning for structured data and its applications, covering from foundational models to open research challenges. Dealing with structured information, such as trees and graphs, requires learning models capable of adapting to structured samples of varying size and topology, capturing the relevant structural patterns to perform predictive and explorative tasks while maintaining the efficiency and scalability necessary to process large scale networks. The tutorial will first introduce foundational aspects and seminal models for learning with structured data (trees, DAGs, graphs). Then it will discuss the most recent advancements in terms of deep learning for structured data processing, including learning structure embeddings, graph convolutions, attentional models. Finally, we will delve into open research challenges, applications, and interesting directions of future research.
Content & Materials
Structured data types: sequences, trees and graphs
Formalization of the learning task: prediction, induction, transduction and generation with structured samples
Historical perspective: recurrent, recursive, contractive and contextual models
Learning with tree-structured data
Recursive gated networks
Generative and Bayesian models for trees
Learning with graph-structured data
Unsupervised learning on graphs
Relevant current research challenges
Graph pooling: global, topological and adaptive methods
Graph generation: adjacency-based, language-based
Expressivity and theoretical aspects
Benchmarks and robust assessment of models
Tutorial slides are available here.
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