A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
A survey on graph structure learning: Progress and opportunities
Graphs are widely used to describe real-world objects and their interactions. Graph Neural
Networks (GNNs) as a de facto model for analyzing graphstructured data, are highly …
Networks (GNNs) as a de facto model for analyzing graphstructured data, are highly …
Every Problem, Every Step, All In Focus: Learning to Solve Vision-Language Problems with Integrated Attention
Integrating information from vision and language modalities has sparked interesting
applications in the fields of computer vision and natural language processing. Existing …
applications in the fields of computer vision and natural language processing. Existing …
VisualHow: Multimodal problem solving
Recent progress in the interdisciplinary studies of computer vision (CV) and natural
language processing (NLP) has enabled the development of intelligent systems that can …
language processing (NLP) has enabled the development of intelligent systems that can …
Deep graph layer information mining convolutional network
Graph convolution network is a powerful method of deep learning of graph structure data.
Existing methods usually adjust the neighborhood information aggregation mode or …
Existing methods usually adjust the neighborhood information aggregation mode or …
Cross-Feature Interactive Tabular Data Modeling With Multiplex Graph Neural Networks
The rising popularity of tabular data in data science applications has led to a surge of
interest in utilizing deep neural networks (DNNs) to address tabular problems. Existing deep …
interest in utilizing deep neural networks (DNNs) to address tabular problems. Existing deep …
Purify and generate: Learning faithful item-to-item graph from noisy user-item interaction behaviors
Matching is almost the first and most fundamental step in recommender systems, that is to
quickly select hundreds or thousands of related entities from the whole commodity pool …
quickly select hundreds or thousands of related entities from the whole commodity pool …
Graph Structure Learning-Based Multivariate Time Series Anomaly Detection in Internet of Things for Human-Centric Consumer Applications
As the Internet of Things system becomes more popular and ubiquitous, it has also gradually
entered the consumer electronics field. For example, smart home systems have numerous …
entered the consumer electronics field. For example, smart home systems have numerous …
Multi-Knowledge Fusion Network for Time Series Representation Learning
SS Sakhinana, S Gupta, KSS Aripirala… - arXiv preprint arXiv …, 2024 - arxiv.org
Forecasting the behaviour of complex dynamical systems such as interconnected sensor
networks characterized by high-dimensional multivariate time series (MTS) is of paramount …
networks characterized by high-dimensional multivariate time series (MTS) is of paramount …
Random graph-based multiple instance learning for structured IoT smart city applications
DKY Chiu, T Xu, I Gondra - ACM Transactions on Internet Technology …, 2021 - dl.acm.org
Because of the complex activities involved in IoT networks of a smart city, an important
question arises: What are the core activities of the networks as a whole and its basic …
question arises: What are the core activities of the networks as a whole and its basic …