1 citations · 1 across the 6 of their papers we have counts for
8 papers
LeanStream: A Speculate-and-Refine Streaming Framework for Efficient on-Device LLM Inference
Renyuan Liu, Yuyang Leng, Kaiyan Liu +7
On-device LLM inference is attractive for privacy and responsiveness, but remains challenging on mobile and embedded devices because model weights far exceed available DRAM. Prior…
Physical Self-Supervised Learning: IMU Sensing without Manual Labels
Yuyang Leng, Renyuan Liu, Shaohan Hu +4
Deep neural networks have become a promising approach for IMU-based sensing, but their scalability is fundamentally limited by costly labeled data and poor robustness to heterogene…
ECA: Efficient Continual Alignment for Open-Ended Image-to-Text Generation
Jiangtao Kong, Peijun Zhao, Chun-Fu Chen +4
Incremental Learning (IL) for Open-ended Image-to-Text Generation (OpenITG) enables models to continuously generate accurate, contextually relevant text for new images while preser…
DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training
Renyuan Liu, Yuyang Leng, Kaiyan Liu +6
Recent advancements in on-device training for deep neural networks have underscored the critical need for efficient activation compression to overcome the memory constraints of mob…
PASS: Private Attributes Protection with Stochastic Data Substitution
Yizhuo Chen, Chun-Fu, Chen +3
The growing Machine Learning (ML) services require extensive collections of user data, which may inadvertently include people's private information irrelevant to the services. Vari…
Model-Agnostic Utility-Preserving Biometric Information Anonymization
Chun-Fu Chen, Bill Moriarty, Shaohan Hu +4
The recent rapid advancements in both sensing and machine learning technologies have given rise to the universal collection and utilization of people's biometrics, such as fingerpr…
MaSS: Multi-attribute Selective Suppression for Utility-preserving Data Transformation from an Information-theoretic Perspective
Yizhuo Chen, Chun-Fu Chen, Hsiang Hsu +3
The growing richness of large-scale datasets has been crucial in driving the rapid advancement and wide adoption of machine learning technologies. The massive collection and usage…
Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation
Hsiang Hsu, Guihong Li, Shaohan Hu +2
Predictive multiplicity refers to the phenomenon in which classification tasks may admit multiple competing models that achieve almost-equally-optimal performance, yet generate con…