85 citations · 263 across the 35 of their papers we have counts for
35 papers
CODAG: Characterizing and Optimizing Decompression Algorithms for GPUs
Jeongmin Park, Zaid Qureshi, Vikram Mailthody +10
Data compression and decompression have become vital components of big-data applications to manage the exponential growth in the amount of data collected and stored. Furthermore, b…
Image Harmonization with Diffusion Model
Jiajie Li, Jian Wang, Chen Wang +1
Image composition in image editing involves merging a foreground image with a background image to create a composite. Inconsistent lighting conditions between the foreground and ba…
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,…
HiKonv: High Throughput Quantized Convolution With Novel Bit-wise Management and Computation
Xinheng Liu, Yao Chen, Prakhar Ganesh +3
Quantization for Convolutional Neural Network (CNN) has shown significant progress with the intention of reducing the cost of computation and storage with low-bitwidth data inputs.…
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…
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks
Shuai Zhang, Meng Wang, Sijia Liu +2
The \textit{lottery ticket hypothesis} (LTH) states that learning on a properly pruned network (the \textit{winning ticket}) improves test accuracy over the original unpruned netwo…
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…
Generic Neural Architecture Search via Regression
Yuhong Li, Cong Hao, Pan Li +2
Most existing neural architecture search (NAS) algorithms are dedicated to and evaluated by the downstream tasks, e.g., image classification in computer vision. However, extensive…
Global Rhythm Style Transfer Without Text Transcriptions
Kaizhi Qian, Yang Zhang, Shiyu Chang +4
Prosody plays an important role in characterizing the style of a speaker or an emotion, but most non-parallel voice or emotion style transfer algorithms do not convert any prosody…
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…
Heterogeneous Contrastive Learning
Lecheng Zheng, Jinjun Xiong, Yada Zhu +1
With the advent of big data across multiple high-impact applications, we are often facing the challenge of complex heterogeneity. The newly collected data usually consist of multip…