37 citations · 151 across the 16 of their papers we have counts for
16 papers
A First Look at On-device Models in iOS Apps
Han Hu, Yujin Huang, Qiuyuan Chen +2
Powered by the rising popularity of deep learning techniques on smartphones, on-device deep learning models are being used in vital fields like finance, social media, and driving a…
A Pairwise Dataset for GUI Conversion and Retrieval between Android Phones and Tablets
Han Hu, Haolan Zhan, Yujin Huang +1
With the popularity of smartphones and tablets, users have become accustomed to using different devices for different tasks, such as using their phones to play games and tablets to…
Automated Mapping of Adaptive App GUIs from Phones to TVs
Han Hu, Ruiqi Dong, John Grundy +3
With the increasing interconnection of smart devices, users often desire to adopt the same app on quite different devices for identical tasks, such as watching the same movies on b…
VanillaKD: Revisit the Power of Vanilla Knowledge Distillation from Small Scale to Large Scale
Zhiwei Hao, Jianyuan Guo, Kai Han +3
The tremendous success of large models trained on extensive datasets demonstrates that scale is a key ingredient in achieving superior results. Therefore, the reflection on the rat…
EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention
Xinyu Liu, Houwen Peng, Ningxin Zheng +3
Vision transformers have shown great success due to their high model capabilities. However, their remarkable performance is accompanied by heavy computation costs, which makes them…
Training-free Lexical Backdoor Attacks on Language Models
Yujin Huang, Terry Yue Zhuo, Qiongkai Xu +3
Large-scale language models have achieved tremendous success across various natural language processing (NLP) applications. Nevertheless, language models are vulnerable to backdoor…
The applicability of transperceptual and deep learning approaches to the study and mimicry of complex cartilaginous tissues
J. Waghorne, C. Howard, H. Hu +4
Complex soft tissues, for example the knee meniscus, play a crucial role in mobility and joint health, but when damaged are incredibly difficult to repair and replace. This is due…
Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning
Phung Lai, Han Hu, NhatHai Phan +3
In this paper, we show that the process of continually learning new tasks and memorizing previous tasks introduces unknown privacy risks and challenges to bound the privacy loss. B…
Deeper Insights into the Robustness of ViTs towards Common Corruptions
Rui Tian, Zuxuan Wu, Qi Dai +2
With Vision Transformers (ViTs) making great advances in a variety of computer vision tasks, recent literature have proposed various variants of vanilla ViTs to achieve better effi…
Semi-Supervised Adversarial Recognition of Refined Window Structures for Inverse Procedural Façade Modeling
Han Hu, Xinrong Liang, Yulin Ding +6
Deep learning methods are notoriously data-hungry, which requires a large number of labeled samples. Unfortunately, the large amount of interactive sample labeling efforts has dram…
Boosting Adversarial Transferability through Enhanced Momentum
Xiaosen Wang, Jiadong Lin, Han Hu +2
Deep learning models are known to be vulnerable to adversarial examples crafted by adding human-imperceptible perturbations on benign images. Many existing adversarial attack metho…
Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps
Yujin Huang, Han Hu, Chunyang Chen
Deep learning has shown its power in many applications, including object detection in images, natural-language understanding, and speech recognition. To make it more accessible to…
Ontology-based Interpretable Machine Learning for Textual Data
Phung Lai, NhatHai Phan, Han Hu +3
In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models. D…
An Ensemble Deep Learning Model for Drug Abuse Detection in Sparse Twitter-Sphere
Han Hu, NhatHai Phan, James Geller +4
As the problem of drug abuse intensifies in the U.S., many studies that primarily utilize social media data, such as postings on Twitter, to study drug abuse-related activities use…
Scalable Differential Privacy with Certified Robustness in Adversarial Learning
NhatHai Phan, My T. Thai, Han Hu +3
In this paper, we aim to develop a scalable algorithm to preserve differential privacy (DP) in adversarial learning for deep neural networks (DNNs), with certified robustness to ad…
Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning
NhatHai Phan, Xintao Wu, Han Hu +1
In this paper, we focus on developing a novel mechanism to preserve differential privacy in deep neural networks, such that: (1) The privacy budget consumption is totally independe…