6 papers
EvoESAP: Non-Uniform Expert Pruning for Sparse MoE
Zongfang Liu, Shengkun Tang, Boyang Sun +2
Sparse Mixture-of-Experts (SMoE) language models achieve strong capability at low per-token compute, yet deployment remains constrained by memory footprint and throughput because t…
Diff-ES: Stage-wise Structural Diffusion Pruning via Evolutionary Search
Zongfang Liu, Shengkun Tang, Zongliang Wu +2
Diffusion models have achieved remarkable success in high-fidelity image generation but remain computationally demanding due to their multi-step denoising process and large model s…
AIMER: Calibration-Free Task-Agnostic MoE Expert Pruning
Zongfang Liu, Guangyi Chen, Shengkun Tang +3
Mixture-of-Experts (MoE) language models increase parameter capacity without proportional per-token computation, yet deployment still requires storing the full expert pool, making…
Unsupervised Synthetic Image Attribution: Alignment and Disentanglement
Zongfang Liu, Guangyi Chen, Boyang Sun +2
As the quality of synthetic images improves, identifying the underlying concepts of model-generated images is becoming increasingly crucial for copyright protection and ensuring mo…
A Sample Efficient Conditional Independence Test in the Presence of Discretization
Boyang Sun, Yu Yao, Xinshuai Dong +4
In many real-world scenarios, interested variables are often represented as discretized values due to measurement limitations. Applying Conditional Independence (CI) tests directly…
Controllable Video Generation with Provable Disentanglement
Yifan Shen, Peiyuan Zhu, Zijian Li +6
Controllable video generation remains a significant challenge, despite recent advances in generating high-quality and consistent videos. Most existing methods for controlling video…