13 citations · 24 across the 7 of their papers we have counts for
7 papers
Scalable Deep Metric Learning on Attributed Graphs
Xiang Li, Gagan Agrawal, Ruoming Jin +1
We consider the problem of constructing embeddings of large attributed graphs and supporting multiple downstream learning tasks. We develop a graph embedding method, which is based…
Federated Contrastive Learning of Graph-Level Representations
Xiang Li, Gagan Agrawal, Rajiv Ramnath +1
Graph-level representations (and clustering/classification based on these representations) are required in a variety of applications. Examples include identifying malicious network…
DocParseNet: Advanced Semantic Segmentation and OCR Embeddings for Efficient Scanned Document Annotation
Ahmad Mohammadshirazi, Ali Nosrati Firoozsalari, Mengxi Zhou +2
Automating the annotation of scanned documents is challenging, requiring a balance between computational efficiency and accuracy. DocParseNet addresses this by combining deep learn…
Recent Advances in Traffic Accident Analysis and Prediction: A Comprehensive Review of Machine Learning Techniques
Noushin Behboudi, Sobhan Moosavi, Rajiv Ramnath
Traffic accidents pose a severe global public health issue, leading to 1.19 million fatalities annually, with the greatest impact on individuals aged 5 to 29 years old. This paper…
CrashFormer: A Multimodal Architecture to Predict the Risk of Crash
Amin Karimi Monsefi, Pouya Shiri, Ahmad Mohammadshirazi +4
Reducing traffic accidents is a crucial global public safety concern. Accident prediction is key to improving traffic safety, enabling proactive measures to be taken before a crash…
Novel Physics-Based Machine-Learning Models for Indoor Air Quality Approximations
Ahmad Mohammadshirazi, Aida Nadafian, Amin Karimi Monsefi +2
Cost-effective sensors are capable of real-time capturing a variety of air quality-related modalities from different pollutant concentrations to indoor/outdoor humidity and tempera…