Embeddings and Representation Learning for Structured Data
Performing machine learning on structured data is complicated by the fact that such data does not have vectorial form. Therefore, multiple approaches have emerged to construct vectorial representations of structured data, from kernel and distance approaches to recurrent, recursive, and convolutional neural networks. Recent years have seen heightened attention in this demanding field of research and several new approaches have emerged, such as metric learning on structured data, graph convolutional neural networks, and recurrent decoder networks for structured data. In this contribution, we provide an high-level overview of the state-of-the-art in representation learning and embeddings for structured data across a wide range of machine learning fields.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningDecoderMetric LearningRepresentation LearningSimilar Papers 제목 키워드 기반
Improving Neural Entity Disambiguation with Graph Embeddings
Entity Disambiguation (ED) is the task of linking an ambiguous entity mention to a corresponding entry in a knowledge base. Current methods have mostly focused on unstructured text data to learn representations of entiti…
ArticlesEntity DisambiguationAutomated Concatenation of Embeddings for Structured Prediction
Pretrained contextualized embeddings are powerful word representations for structured prediction tasks. Recent work found that better word representations can be obtained by concatenating different types of embeddings. H…
Aspect ExtractionChunkingDependency ParsingNamed Entity Recognition (NER)+5Learning Structured Visual Compositional Representations for Weakly Supervised Referring Expression Comprehension
Referring expression comprehension (REC) aims to localize the object in an image described by natural language. In Weakly supervised REC (WREC), existing approaches primarily operate on anchor-level visual representation…
Referring ExpressionHierarchical Lexical Manifold Projection in Large Language Models: A Novel Mechanism for Multi-Scale Semantic Representation
The integration of structured hierarchical embeddings into transformer-based architectures introduces a refined approach to lexical representation, ensuring that multi-scale semantic relationships are preserved without c…
Adversarial TextComputational EfficiencyDomain Specific Data Distillation and Multi-modal Embedding Generation
The challenge of creating domain-centric embeddings arises from the abundance of unstructured data and the scarcity of domain-specific structured data. Conventional embedding techniques often rely on either modality, lim…
AttributeCloud ComputingCollaborative FilteringPrediction