5 citations · 5 across the 6 of their papers we have counts for
7 papers
OLIVE: Online Low-Rank Incremental Learning for Efficient Adaptive Exoskeletons
Dong Liu, Yanxuan Yu, Ben Lengerich +2
Wearable exoskeleton systems hold promise for restoring mobility in individuals with physical impairments, yet most existing controllers rely on static gait policies that cannot ad…
Compensation-free Machine Unlearning in Text-to-Image Diffusion Models by Eliminating the Mutual Information
Xinwen Cheng, Jingyuan Zhang, Zhehao Huang +2
The powerful generative capabilities of diffusion models have raised growing privacy and safety concerns regarding generating sensitive or undesired content. In response, machine u…
AdaCorrection: Adaptive Offset Cache Correction for Accurate Diffusion Transformers
Dong Liu, Yanxuan Yu, Ben Lengerich +1
Diffusion Transformers (DiTs) achieve state-of-the-art performance in high-fidelity image and video generation but suffer from expensive inference due to their iterative denoising…
Compute Only Once: UG-Separation for Efficient Large Recommendation Models
Hui Lu, Zheng Chai, Shipeng Bai +15
Driven by scaling laws, recommender systems increasingly rely on larger-scale models to capture complex feature interactions and user behaviors, but this trend also leads to prohib…
PiKV: KV Cache Management System for Mixture of Experts
Dong Liu, Yanxuan Yu, Ben Lengerich +1
As large-scale language models continue to scale up in both size and context length, the memory and communication cost of key-value (KV) cache storage has become a major bottleneck…
FastCache: Fast Caching for Diffusion Transformer Through Learnable Linear Approximation
Dong Liu, Yanxuan Yu, Jiayi Zhang +3
Diffusion Transformers (DiT) are powerful generative models but remain computationally intensive due to their iterative structure and deep transformer stacks. To alleviate this ine…
Designing Large Foundation Models for Efficient Training and Inference: A Survey
Dong Liu, Yanxuan Yu, Yite Wang +5
This paper focuses on modern efficient training and inference technologies on foundation models and illustrates them from two perspectives: model and system design. Model and Syste…