Exploring causal learning through graph neural networks: an in-depth review
In machine learning, exploring data correlations to predict outcomes is a fundamental task.
Recognizing causal relationships embedded within data is pivotal for a comprehensive …
Recognizing causal relationships embedded within data is pivotal for a comprehensive …
Correlated time series self-supervised representation learning via spatiotemporal bootstrapping
Correlated time series analysis plays an important role in many real-world industries.
Learning an efficient representation of this large-scale data for further downstream tasks is …
Learning an efficient representation of this large-scale data for further downstream tasks is …
Mm-dag: Multi-task dag learning for multi-modal data-with application for traffic congestion analysis
This paper proposes to learn Multi-task, Multi-modal Direct Acyclic Graphs (MM-DAGs),
which are commonly observed in complex systems, eg, traffic, manufacturing, and weather …
which are commonly observed in complex systems, eg, traffic, manufacturing, and weather …
From data to action in flood forecasting leveraging graph neural networks and digital twin visualization
NS Roudbari, SR Punekar, Z Patterson, U Eicker… - Scientific reports, 2024 - nature.com
Forecasting floods encompasses significant complexity due to the nonlinear nature of
hydrological systems, which involve intricate interactions among precipitation, landscapes …
hydrological systems, which involve intricate interactions among precipitation, landscapes …
Adaptive hierarchical spatiotemporal network for traffic forecasting
Accurate traffic forecasting is vital to intelligent transportation systems, which are widely
adopted to solve urban traffic issues. Existing traffic forecasting studies focus on modeling …
adopted to solve urban traffic issues. Existing traffic forecasting studies focus on modeling …
VisionTraj: A Noise-Robust Trajectory Recovery Framework based on Large-scale Camera Network
Trajectory recovery based on the snapshots from the city-wide multi-camera network
facilitates urban mobility sensing and driveway optimization. The state-of-the-art solutions …
facilitates urban mobility sensing and driveway optimization. The state-of-the-art solutions …
KGTS: Contrastive Trajectory Similarity Learning over Prompt Knowledge Graph Embedding
Trajectory similarity computation serves as a fundamental functionality of various spatial
information applications. Although existing deep learning similarity computation methods …
information applications. Although existing deep learning similarity computation methods …
Gated fusion adaptive graph neural network for urban road traffic flow prediction
L Xiong, X Yuan, Z Hu, X Huang, P Huang - Neural Processing Letters, 2024 - Springer
Accurate prediction of traffic flow plays an important role in maintaining traffic order and
traffic safety, which is a key task in the application of intelligent transportation systems (ITS) …
traffic safety, which is a key task in the application of intelligent transportation systems (ITS) …
An Intelligent Deep Learning Framework for Traffic Flow Imputation and Short-term Prediction Based on Dynamic Features
The accurate prediction of traffic flow has emerged as a focal point in the cutting-edge
sphere of intelligent transportation. Extant methodologies rely on deep learning for short …
sphere of intelligent transportation. Extant methodologies rely on deep learning for short …
Skin Cancer Detection utilizing Deep Learning: Classification of Skin Lesion Images using a Vision Transformer
C Flosdorf, J Engelker, I Keller, N Mohr - arXiv preprint arXiv:2407.18554, 2024 - arxiv.org
Skin cancer detection still represents a major challenge in healthcare. Common detection
methods can be lengthy and require human assistance which falls short in many countries …
methods can be lengthy and require human assistance which falls short in many countries …