85 citations · 263 across the 35 of their papers we have counts for
12 papers · 1 filter
Parallelizing Maximal Clique Enumeration on GPUs
Mohammad Almasri, Yen-Hsiang Chang, Izzat El Hajj +3
We present a GPU solution for exact maximal clique enumeration (MCE) that performs a search tree traversal following the Bron-Kerbosch algorithm. Prior works on parallelizing MCE o…
A Moral- and Event- Centric Inspection of Gender Bias in Fairy Tales at A Large Scale
Zhixuan Zhou, Jiao Sun, Jiaxin Pei +2
Fairy tales are a common resource for young children to learn a language or understand how a society works. However, gender bias, e.g., stereotypical gender roles, in this literatu…
Submission-Aware Reviewer Profiling for Reviewer Recommender System
Omer Anjum, Alok Kamatar, Toby Liang +2
Assigning qualified, unbiased and interested reviewers to paper submissions is vital for maintaining the integrity and quality of the academic publishing system and providing valua…
Excel Spreadsheet Analyzer
Amir Nassereldine, Patrick Chen, Jinjun Xiong
Spreadsheets are widely used in various fields to do large numerical analysis. While several companies have relied on spreadsheets for decades, data scientists are going in the dir…
Extensible Proxy for Efficient NAS
Yuhong Li, Jiajie Li, Cong Han +3
Neural Architecture Search (NAS) has become a de facto approach in the recent trend of AutoML to design deep neural networks (DNNs). Efficient or near-zero-cost NAS proxies are fur…
Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT
Bhavya Bhavya, Jinjun Xiong, Chengxiang Zhai
We propose a novel application of prompting Pre-trained Language Models (PLMs) to generate analogies and study how to design effective prompts for two task settings: generating a s…
Can Language Models Be Specific? How?
Jie Huang, Kevin Chen-Chuan Chang, Jinjun Xiong +1
"He is a person", "Paris is located on the earth". Both statements are correct but meaningless - due to lack of specificity. In this paper, we propose to measure how specific the l…
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling
Hongkang Li, Meng Wang, Sijia Liu +2
Graph convolutional networks (GCNs) have recently achieved great empirical success in learning graph-structured data. To address its scalability issue due to the recursive embeddin…
DEER: Descriptive Knowledge Graph for Explaining Entity Relationships
Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang +2
We propose DEER (Descriptive Knowledge Graph for Explaining Entity Relationships) - an open and informative form of modeling entity relationships. In DEER, relationships between en…
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions
Yong Xie, Dakuo Wang, Pin-Yu Chen +3
More and more investors and machine learning models rely on social media (e.g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements.…
GPU-Initiated On-Demand High-Throughput Storage Access in the BaM System Architecture
Zaid Qureshi, Vikram Sharma Mailthody, Isaac Gelado +10
Graphics Processing Units (GPUs) have traditionally relied on the host CPU to initiate access to the data storage. This approach is well-suited for GPU applications with known data…
How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
Shuai Zhang, Meng Wang, Sijia Liu +2
Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited. Despite empirical successes,…