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Found 18 papers, 9 papers with code
Is This a Bad Table? A Closer Look at the Evaluation of Table Generation from Text
Understanding whether a generated table is of good quality is important to be able to use it in creating or editing documents using automatic methods.
Enhancing Presentation Slide Generation by LLMs with a Multi-Staged End-to-End Approach
Generating presentation slides from a long document with multimodal elements such as text and images is an important task.
PostDoc: Generating Poster from a Long Multimodal Document Using Deep Submodular Optimization
A poster from a long input document can be considered as a one-page easy-to-read multimodal (text and images) summary presented on a nice template with good design elements.
Presentations are not always linear! GNN meets LLM for Document-to-Presentation Transformation with Attribution
Automatically generating a presentation from the text of a long document is a challenging and useful problem.
Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search
This paper introduces the "Shopping Queries Dataset", a large dataset of difficult Amazon search queries and results, publicly released with the aim of fostering research in improving the quality of search results.
Monolith to Microservices: Representing Application Software through Heterogeneous Graph Neural Network
But the challenges associated with the separation of functional modules, slows down the migration of a monolithic code into microservices.
Graph Neural Network to Dilute Outliers for Refactoring Monolith Application
Therefore, this problem of refactoring can be viewed as a graph based clustering task.
Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture
Soil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation.
Unsupervised Constrained Community Detection via Self-Expressive Graph Neural Network
Designing an unsupervised loss function to train a GNN and extract communities in an integrated manner is a fundamental challenge.
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph Description
Real world networks often come with (community) outlier nodes, which behave differently from the regular nodes of the community.
Robust Hierarchical Graph Classification with Subgraph Attention
Towards this end, we propose a graph classification algorithm called SubGattPool which jointly learns the subgraph attention and employs two different types of hierarchical attention mechanisms to find the important nodes in a hierarchy and the importance of individual hierarchies in a graph.
Unsupervised Graph Representation by Periphery and Hierarchical Information Maximization
Invent of graph neural networks has improved the state-of-the-art for both node and the entire graph representation in a vector space.
Line Hypergraph Convolution Network: Applying Graph Convolution for Hypergraphs
Then we propose to use graph convolution on the line graph of a hypergraph.
Beyond Node Embedding: A Direct Unsupervised Edge Representation Framework for Homogeneous Networks
To the best of our knowledge, this is the first direct unsupervised approach for edge embedding in homogeneous information networks, without relying on the node embeddings.
Subgraph Attention for Node Classification and Hierarchical Graph Pooling
Along with attention over the subgraphs, our pooling architecture also uses attention to determine the important nodes within a level graph and attention to determine the important levels in the whole hierarchy.
Outlier Aware Network Embedding for Attributed Networks
We also consider different downstream machine learning applications on networks to show the efficiency of ONE as a generic network embedding technique.
FSCNMF: Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks
In this work, we propose a nonnegative matrix factorization based optimization framework, namely FSCNMF which considers both the network structure and the content of the nodes while learning a lower dimensional vector representation of each node in the network.
Social and Information Networks
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks
It is not straightforward to integrate the content of each node in the current state-of-the-art network embedding methods.
