12 citations · 20 across the 4 of their papers we have counts for
5 papers
Apto: Self-Adaptive CPU-GPU Hybrid MoE Inference on Shared PCs
Zihang Zhong, Jiali Wang, Rong Chen
CPU/GPU hybrid inference enables Mixture-of-Experts (MoE) models to run on memory-constrained PCs by keeping all expert weights in host memory and executing activated experts acros…
Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data
Zhiqiang Tang, Zihan Zhong, Tong He +1
This paper studies the best practices for automatic machine learning (AutoML). While previous AutoML efforts have predominantly focused on unimodal data, the multimodal aspect rema…
ProMoE: Fast MoE-based LLM Serving using Proactive Caching
Xiaoniu Song, Zihang Zhong, Rong Chen +1
The promising applications of large language models are often limited by the constrained GPU memory capacity available on edge devices. Mixture-of-Experts (MoE) models help address…
AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
Zhiqiang Tang, Haoyang Fang, Su Zhou +5
AutoGluon-Multimodal (AutoMM) is introduced as an open-source AutoML library designed specifically for multimodal learning. Distinguished by its exceptional ease of use, AutoMM ena…
Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
Zihan Zhong, Zhiqiang Tang, Tong He +2
The Segment Anything Model (SAM) stands as a foundational framework for image segmentation. While it exhibits remarkable zero-shot generalization in typical scenarios, its advantag…