333 citations · 1.3k across the 71 of their papers we have counts for
19 papers · 1 filter
Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction
Zhenguang Liu, Shuang Wu, Shuyuan Jin +4
Predicting human motion from historical pose sequence is crucial for a machine to succeed in intelligent interactions with humans. One aspect that has been obviated so far, is the…
NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification
Haibin Zheng, Zhiqing Chen, Tianyu Du +6
Deep neural networks (DNNs) have demonstrated their outperformance in various domains. However, it raises a social concern whether DNNs can produce reliable and fair decisions espe…
Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models
Jialuo Chen, Jingyi Wang, Tinglan Peng +6
Deep learning (DL) models, especially those large-scale and high-performance ones, can be very costly to train, demanding a great amount of data and computational resources. Unauth…
Fight Perturbations with Perturbations: Defending Adversarial Attacks via Neuron Influence
Ruoxi Chen, Haibo Jin, Haibin Zheng +2
The vulnerabilities of deep learning models towards adversarial attacks have attracted increasing attention, especially when models are deployed in security-critical domains. Numer…
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art
Xiang Ling, Lingfei Wu, Jiangyu Zhang +9
Malware has been one of the most damaging threats to computers that span across multiple operating systems and various file formats. To defend against ever-increasing and ever-evol…
Towards Robust Reasoning over Knowledge Graphs
Zhaohan Xi, Ren Pang, Changjiang Li +4
Answering complex logical queries over large-scale knowledge graphs (KGs) represents an important artificial intelligence task, entailing a range of applications. Recently, knowled…
Backdoor Pre-trained Models Can Transfer to All
Lujia Shen, Shouling Ji, Xuhong Zhang +6
Pre-trained general-purpose language models have been a dominating component in enabling real-world natural language processing (NLP) applications. However, a pre-trained model wit…
AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy
Linkang Du, Zhikun Zhang, Shaojie Bai +4
For protecting users' private data, local differential privacy (LDP) has been leveraged to provide the privacy-preserving range query, thus supporting further statistical analysis.…
On the Security Risks of AutoML
Ren Pang, Zhaohan Xi, Shouling Ji +2
Neural Architecture Search (NAS) represents an emerging machine learning (ML) paradigm that automatically searches for models tailored to given tasks, which greatly simplifies the…
GGT: Graph-Guided Testing for Adversarial Sample Detection of Deep Neural Network
Zuohui Chen, Renxuan Wang, Jingyang Xiang +5
Deep Neural Networks (DNN) are known to be vulnerable to adversarial samples, the detection of which is crucial for the wide application of these DNN models. Recently, a number of…
Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion
Zhenguang Liu, Peng Qian, Xiang Wang +3
Smart contracts hold digital coins worth billions of dollars, their security issues have drawn extensive attention in the past years. Towards smart contract vulnerability detection…
Constructing Contrastive samples via Summarization for Text Classification with limited annotations
Yangkai Du, Tengfei Ma, Lingfei Wu +4
Contrastive Learning has emerged as a powerful representation learning method and facilitates various downstream tasks especially when supervised data is limited. How to construct…
Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning
Jianfeng Dong, Zhe Ma, Xiaofeng Mao +4
This paper strives to predict fine-grained fashion similarity. In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute…
Deep Dual Consecutive Network for Human Pose Estimation
Zhenguang Liu, Haoming Chen, Runyang Feng +4
Multi-frame human pose estimation in complicated situations is challenging. Although state-of-the-art human joints detectors have demonstrated remarkable results for static images,…
Aggregated Multi-GANs for Controlled 3D Human Motion Prediction
Zhenguang Liu, Kedi Lyu, Shuang Wu +3
Human motion prediction from historical pose sequence is at the core of many applications in machine intelligence. However, in current state-of-the-art methods, the predicted futur…
EfficientTDNN: Efficient Architecture Search for Speaker Recognition
Rui Wang, Zhihua Wei, Haoran Duan +3
Convolutional neural networks (CNNs), such as the time-delay neural network (TDNN), have shown their remarkable capability in learning speaker embedding. However, they meanwhile br…
Enhancing Model Robustness By Incorporating Adversarial Knowledge Into Semantic Representation
Jinfeng Li, Tianyu Du, Xiangyu Liu +3
Despite that deep neural networks (DNNs) have achieved enormous success in many domains like natural language processing (NLP), they have also been proven to be vulnerable to malic…
Hierarchical Similarity Learning for Language-based Product Image Retrieval
Zhe Ma, Fenghao Liu, Jianfeng Dong +3
This paper aims for the language-based product image retrieval task. The majority of previous works have made significant progress by designing network structure, similarity measur…
i-Algebra: Towards Interactive Interpretability of Deep Neural Networks
Xinyang Zhang, Ren Pang, Shouling Ji +2
Providing explanations for deep neural networks (DNNs) is essential for their use in domains wherein the interpretability of decisions is a critical prerequisite. Despite the pleth…