19 citations · 33 across the 11 of their papers we have counts for
11 papers
EMO: Energy Efficiency Modeling and Optimization for AI Workloads
Jiyu Luo, Shaoyu Chen, Jingwei Sun +3
The massive energy consumption of GPU-accelerated AI workloads challenges sustainable computing. We observe that execution asynchrony (e.g., CPU-GPU, concurrent streams, multi-GPU)…
Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling
Jiahao Wu, Wenqi Fan, Shengcai Liu +3
Graph-based collaborative filtering has emerged as a powerful paradigm for delivering personalized recommendations. Despite their demonstrated effectiveness, these methods often ne…
Neural Influence Estimator: Towards Real-time Solutions to Influence Blocking Maximization
Wenjie Chen, Shengcai Liu, Yew-Soon Ong +2
Real-time solutions to the influence blocking maximization (IBM) problems are crucial for promptly containing the spread of misinformation. However, achieving this goal is non-triv…
Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and Applications
Xuanfeng Li, Shengcai Liu, Jin Wang +3
The multiple-choice knapsack problem (MCKP) is a classic NP-hard combinatorial optimization problem. Motivated by several significant real-world applications, this work investigate…
Large Language Models can be Guided to Evade AI-Generated Text Detection
Ning Lu, Shengcai Liu, Rui He +3
Large language models (LLMs) have shown remarkable performance in various tasks and have been extensively utilized by the public. However, the increasing concerns regarding the mis…
Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend
Ning Lu, Shengcai Liu, Zhirui Zhang +3
Word-level textual adversarial attacks have demonstrated notable efficacy in misleading Natural Language Processing (NLP) models. Despite their success, the underlying reasons for…
Reliable Robustness Evaluation via Automatically Constructed Attack Ensembles
Shengcai Liu, Fu Peng, Ke Tang
Attack Ensemble (AE), which combines multiple attacks together, provides a reliable way to evaluate adversarial robustness. In practice, AEs are often constructed and tuned by huma…
Disentangled Contrastive Learning for Social Recommendation
Jiahao Wu, Wenqi Fan, Jingfan Chen +3
Social recommendations utilize social relations to enhance the representation learning for recommendations. Most social recommendation models unify user representations for the use…
GANDSE: Generative Adversarial Network based Design Space Exploration for Neural Network Accelerator Design
Lang Feng, Wenjian Liu, Chuliang Guo +3
With the popularity of deep learning, the hardware implementation platform of deep learning has received increasing interest. Unlike the general purpose devices, e.g., CPU, or GPU,…
Defending Adversarial Examples by Negative Correlation Ensemble
Wenjian Luo, Hongwei Zhang, Linghao Kong +2
The security issues in DNNs, such as adversarial examples, have attracted much attention. Adversarial examples refer to the examples which are capable to induce the DNNs return com…
Saliency Attack: Towards Imperceptible Black-box Adversarial Attack
Zeyu Dai, Shengcai Liu, Ke Tang +1
Deep neural networks are vulnerable to adversarial examples, even in the black-box setting where the attacker is only accessible to the model output. Recent studies have devised ef…