83 citations · 613 across the 55 of their papers we have counts for
10 papers · 1 filter
Graph Neural Network Training with Data Tiering
Seung Won Min, Kun Wu, Mert Hidayetoğlu +3
Graph Neural Networks (GNNs) have shown success in learning from graph-structured data, with applications to fraud detection, recommendation, and knowledge graph reasoning. However…
MLHarness: A Scalable Benchmarking System for MLCommons
Yen-Hsiang Chang, Jianhao Pu, Wen-mei Hwu +1
With the society's growing adoption of machine learning (ML) and deep learning (DL) for various intelligent solutions, it becomes increasingly imperative to standardize a common se…
Understanding Jargon: Combining Extraction and Generation for Definition Modeling
Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang +2
Can machines know what twin prime is? From the composition of this phrase, machines may guess twin prime is a certain kind of prime, but it is still difficult to deduce exactly wha…
Open Relation Modeling: Learning to Define Relations between Entities
Jie Huang, Kevin Chen-Chuan Chang, Jinjun Xiong +1
Relations between entities can be represented by different instances, e.g., a sentence containing both entities or a fact in a Knowledge Graph (KG). However, these instances may no…
Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach
Jie Huang, Kevin Chen-Chuan Chang, Jinjun Xiong +1
We propose to measure fine-grained domain relevance - the degree that a term is relevant to a broad (e.g., computer science) or narrow (e.g., deep learning) domain. Such measuremen…
Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection
Jiachen Li, Bowen Cheng, Rogerio Feris +4
Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods, which limits their potential in competing with classic anchor-based…
Parallel K-Clique Counting on GPUs
Mohammad Almasri, Izzat El Hajj, Rakesh Nagi +2
Counting k-cliques in a graph is an important problem in graph analysis with many applications such as community detection and graph partitioning. Counting k-cliques is typically d…
Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture
Seung Won Min, Kun Wu, Sitao Huang +5
Graph Convolutional Networks (GCNs) are increasingly adopted in large-scale graph-based recommender systems. Training GCN requires the minibatch generator traversing graphs and sam…
PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses
Seung Won Min, Kun Wu, Sitao Huang +5
With the increasing adoption of graph neural networks (GNNs) in the machine learning community, GPUs have become an essential tool to accelerate GNN training. However, training GNN…
Safer Illinois and RokWall: Privacy Preserving University Health Apps for COVID-19
Vikram Sharma Mailthody, James Wei, Nicholas Chen +14
COVID-19 has fundamentally disrupted the way we live. Government bodies, universities, and companies worldwide are rapidly developing technologies to combat the COVID-19 pandemic a…