7 citations · 10 across the 11 of their papers we have counts for
6 papers · 1 filter
ZEUS: Accelerating Diffusion Models with Only Second-Order Predictor
Yixiao Wang, Ting Jiang, Zishan Shao +6
Denoising generative models deliver high-fidelity generation but remain bottlenecked by inference latency due to the many iterative denoiser calls required during sampling. Trainin…
Swimba: Switch Mamba Model Scales State Space Models
Zhixu Du, Krishna Teja Chitty-Venkata, Murali Emani +3
Mixture-of-experts (MoE) is a common approach for increasing parameter capacity, but applying MoE to state space model (SSM) token mixers can multiply the cost of the recurrent sta…
SADA: Stability-guided Adaptive Diffusion Acceleration
Ting Jiang, Yixiao Wang, Hancheng Ye +7
Diffusion models have achieved remarkable success in generative tasks but suffer from high computational costs due to their iterative sampling process and quadratic attention costs…
Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity
Yide Ran, Wentao Guo, Jingwei Sun +7
Federated Learning enables collaborative fine-tuning of Large Language Models (LLMs) across decentralized Non-Independent and Identically Distributed (Non-IID) clients, but such mo…
Keyframe-oriented Vision Token Pruning: Enhancing Efficiency of Large Vision Language Models on Long-Form Video Processing
Yudong Liu, Jingwei Sun, Yueqian Lin +6
Vision language models (VLMs) demonstrate strong capabilities in jointly processing visual and textual data. However, they often incur substantial computational overhead due to red…
SiDA-MoE: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models
Zhixu Du, Shiyu Li, Yuhao Wu +7
Mixture-of-Experts (MoE) has emerged as a favorable architecture in the era of large models due to its inherent advantage, i.e., enlarging model capacity without incurring notable…