34 citations · 123 across the 15 of their papers we have counts for
15 papers
Beta Distribution Learning for Reliable Roadway Crash Risk Assessment
Ahmad Elallaf, Nathan Jacobs, Xinyue Ye +2
Roadway traffic accidents represent a global health crisis, responsible for over a million deaths annually and costing many countries up to 3% of their GDP. Traditional traffic saf…
Mutation-Based Adversarial Attacks on Neural Text Detectors
Gongbo Liang, Jesus Guerrero, Izzat Alsmadi
Neural text detectors aim to decide the characteristics that distinguish neural (machine-generated) from human texts. To challenge such detectors, adversarial attacks can alter the…
A Mutation-based Text Generation for Adversarial Machine Learning Applications
Jesus Guerrero, Gongbo Liang, Izzat Alsmadi
Many natural language related applications involve text generation, created by humans or machines. While in many of those applications machines support humans, yet in few others, (…
Estimating Cluster Masses from SDSS Multi-band Images with Transfer Learning
Sheng-Chieh Lin, Yuanyuan Su, Gongbo Liang +3
The total masses of galaxy clusters characterize many aspects of astrophysics and the underlying cosmology. It is crucial to obtain reliable and accurate mass estimates for numerou…
Benchmark Assessment for DeepSpeed Optimization Library
Gongbo Liang, Izzat Alsmadi
Deep Learning (DL) models are widely used in machine learning due to their performance and ability to deal with large datasets while producing high accuracy and performance metrics…
Dynamic Feature Alignment for Semi-supervised Domain Adaptation
Yu Zhang, Gongbo Liang, Nathan Jacobs
Most research on domain adaptation has focused on the purely unsupervised setting, where no labeled examples in the target domain are available. However, in many real-world scenari…
Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate
Gongbo Liang, Yuanyuan Su, Sheng-Chieh Lin +3
Most galaxies in the nearby Universe are gravitationally bound to a cluster or group of galaxies. Their optical contents, such as optical richness, are crucial for understanding th…
Dynamic Image for 3D MRI Image Alzheimer's Disease Classification
Xin Xing, Gongbo Liang, Hunter Blanton +4
We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally…
Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging
Gongbo Liang, Connor Greenwell, Yu Zhang +3
A key challenge in training neural networks for a given medical imaging task is often the difficulty of obtaining a sufficient number of manually labeled examples. In contrast, tex…
Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification
Gongbo Liang, Yu Zhang, Xiaoqin Wang +1
Recent works have shown that deep neural networks can achieve super-human performance in a wide range of image classification tasks in the medical imaging domain. However, these wo…
A deep learning view of the census of galaxy clusters in IllustrisTNG
Y. Su, Y. Zhang, G. Liang +8
The origin of the diverse population of galaxy clusters remains an unexplained aspect of large-scale structure formation and cluster evolution. We present a novel method of using X…
Unsupervised Domain Adaptation for Mammogram Image Classification: A Promising Tool for Model Generalization
Yu Zhang, Gongbo Liang, Nathan Jacobs +1
Generalization is one of the key challenges in the clinical validation and application of deep learning models to medical images. Studies have shown that such models trained on pub…
Joint 2D-3D Breast Cancer Classification
Gongbo Liang, Xiaoqin Wang, Yu Zhang +4
Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D…
2D Convolutional Neural Networks for 3D Digital Breast Tomosynthesis Classification
Yu Zhang, Xiaoqin Wang, Hunter Blanton +3
Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical pr…
Defense-PointNet: Protecting PointNet Against Adversarial Attacks
Yu Zhang, Gongbo Liang, Tawfiq Salem +1
Despite remarkable performance across a broad range of tasks, neural networks have been shown to be vulnerable to adversarial attacks. Many works focus on adversarial attacks and d…