49 citations · 279 across the 48 of their papers we have counts for
10 papers · 1 filter
BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens
Hao Wen, Xinrui Wu, Yi Sun +7
Recent advancements in Large Language Models (LLMs) have leveraged increased test-time computation to enhance reasoning capabilities, a strategy that, while effective, incurs signi…
Threshold Neuron: A Brain-inspired Artificial Neuron for Efficient On-device Inference
Zihao Zheng, Yuanchun Li, Jiayu Chen +3
Enhancing the computational efficiency of on-device Deep Neural Networks (DNNs) remains a significant challengein mobile and edge computing. As we aim to execute increasingly compl…
Anatomizing Deep Learning Inference in Web Browsers
Qipeng Wang, Shiqi Jiang, Zhenpeng Chen +6
Web applications have increasingly adopted Deep Learning (DL) through in-browser inference, wherein DL inference performs directly within Web browsers. The actual performance of in…
A Survey of Resource-efficient LLM and Multimodal Foundation Models
Mengwei Xu, Wangsong Yin, Dongqi Cai +15
Large foundation models, including large language models (LLMs), vision transformers (ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine lear…
PatchBackdoor: Backdoor Attack against Deep Neural Networks without Model Modification
Yizhen Yuan, Rui Kong, Shenghao Xie +2
Backdoor attack is a major threat to deep learning systems in safety-critical scenarios, which aims to trigger misbehavior of neural network models under attacker-controlled condit…
AdaptiveNet: Post-deployment Neural Architecture Adaptation for Diverse Edge Environments
Hao Wen, Yuanchun Li, Zunshuai Zhang +5
Deep learning models are increasingly deployed to edge devices for real-time applications. To ensure stable service quality across diverse edge environments, it is highly desirable…
FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients
Jaemin Shin, Yuanchun Li, Yunxin Liu +1
Federated Learning (FL) trains a machine learning model on distributed clients without exposing individual data. Unlike centralized training that is usually based on carefully-orga…
DistFL: Distribution-aware Federated Learning for Mobile Scenarios
Bingyan Liu, Yifeng Cai, Ziqi Zhang +5
Federated learning (FL) has emerged as an effective solution to decentralized and privacy-preserving machine learning for mobile clients. While traditional FL has demonstrated its…
Representational Continuity for Unsupervised Continual Learning
Divyam Madaan, Jaehong Yoon, Yuanchun Li +2
Continual learning (CL) aims to learn a sequence of tasks without forgetting the previously acquired knowledge. However, recent CL advances are restricted to supervised continual l…
ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection
Yuanchun Li, Ziqi Zhang, Bingyan Liu +2
The knowledge of a deep learning model may be transferred to a student model, leading to intellectual property infringement or vulnerability propagation. Detecting such knowledge r…