3 papers
cs.CV2026
Roots Beneath the Cut: Uncovering the Risk of Concept Revival in Pruning-Based Unlearning for Diffusion Models
Ci Zhang, Zhaojun Ding, Chence Yang +7
Pruning-based unlearning has recently emerged as a fast, training-free, and data-independent approach to remove undesired concepts from diffusion models. It promises high efficienc…
cs.LG2025
End-to-End On-Device Quantization-Aware Training for LLMs at Inference Cost
Qitao Tan, Xiaoying Song, Jin Lu +9
Quantization is an effective technique to reduce the deployment cost of large language models (LLMs), and post-training quantization (PTQ) has been widely studied due to its effici…
cs.LG2025
Perturbation-efficient Zeroth-order Optimization for Hardware-friendly On-device Training
Qitao Tan, Sung-En Chang, Rui Xia +10
Zeroth-order (ZO) optimization is an emerging deep neural network (DNN) training paradigm that offers computational simplicity and memory savings. However, this seemingly promising…