9 papers
TRACE: Transition-Aware Residual Control for Multi-Objective Materials Discovery
Kang Zhou, Yujia Tong, Yong Tao +1
Multi-objective materials discovery with LLM agents is often limited not only by how many candidates can be proposed, but by how effectively each costly property evaluation informs…
Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction
Yujia Tong, Yuxi Wang, Yunyang Wan +3
Model compression techniques such as quantization and pruning are widely used to reduce the deployment cost of large language models (LLMs), with existing evaluations focusing almo…
Forget by Uncertainty: Orthogonal Entropy Unlearning for Quantized Neural Networks
Tian Zhang, Yujia Tong, Junhao Dong +3
The deployment of quantized neural networks on edge devices, combined with privacy regulations like GDPR, creates an urgent need for machine unlearning in quantized models. However…
SAGE: Accelerating Vision-Language Models via Entropy-Guided Adaptive Speculative Decoding
Yujia Tong, Tian Zhang, Yunyang Wan +3
Speculative decoding has emerged as a promising approach to accelerate inference in vision-language models (VLMs) by enabling parallel verification of multiple draft tokens. Howeve…
Heaven-Sent or Hell-Bent? Benchmarking the Intelligence and Defectiveness of LLM Hallucinations
Chengxu Yang, Jingling Yuan, Siqi Cai +2
Hallucinations in large language models (LLMs) are commonly regarded as errors to be minimized. However, recent perspectives suggest that some hallucinations may encode creative or…
LetheViT: Selective Machine Unlearning for Vision Transformers via Attention-Guided Contrastive Learning
Yujia Tong, Tian Zhang, Jingling Yuan +2
Vision Transformers (ViTs) have revolutionized computer vision tasks with their exceptional performance. However, the introduction of privacy regulations such as GDPR and CCPA has…
DFQ-ViT: Data-Free Quantization for Vision Transformers without Fine-tuning
Yujia Tong, Jingling Yuan, Tian Zhang +2
Data-Free Quantization (DFQ) enables the quantization of Vision Transformers (ViTs) without requiring access to data, allowing for the deployment of ViTs on devices with limited re…
Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction
Yujia Tong, Jingling Yuan, Chuang Hu
With the development of mobile and edge computing, the demand for low-bit quantized models on edge devices is increasing to achieve efficient deployment. To enhance the performance…
Robust Machine Unlearning for Quantized Neural Networks via Adaptive Gradient Reweighting with Similar Labels
Yujia Tong, Yuze Wang, Jingling Yuan +1
Model quantization enables efficient deployment of deep neural networks on edge devices through low-bit parameter representation, yet raises critical challenges for implementing ma…