200 citations · 549 across the 29 of their papers we have counts for
10 papers · 1 filter
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…
Best-of-Better-: Generating Pre-Aligned Responses with In-Context Learning
Eric Lei, Hsiang Hsu, Chun-Fu Chen
Inference-time alignment methods, such as Best-of-, offer a flexible alternative to training-based alignment by using reward models to select high-quality responses generated by…
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…
Machine Unlearning for Image-to-Image Generative Models
Guihong Li, Hsiang Hsu, Chun-Fu Chen +1
Machine unlearning has emerged as a new paradigm to deliberately forget data samples from a given model in order to adhere to stringent regulations. However, existing machine unlea…
OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learning
Wei-Cheng Huang, Chun-Fu Chen, Hsiang Hsu
Recent works have shown that by using large pre-trained models along with learnable prompts, rehearsal-free methods for class-incremental learning (CIL) settings can achieve superi…
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…
Fast-NTK: Parameter-Efficient Unlearning for Large-Scale Models
Guihong Li, Hsiang Hsu, Chun-Fu Chen +1
The rapid growth of machine learning has spurred legislative initiatives such as ``the Right to be Forgotten,'' allowing users to request data removal. In response, ``machine unlea…
MaSS: Multi-attribute Selective Suppression
Chun-Fu Chen, Shaohan Hu, Zhonghao Shi +5
The recent rapid advances in machine learning technologies largely depend on the vast richness of data available today, in terms of both the quantity and the rich content contained…