7 citations · 7 across the 2 of their papers we have counts for
3 papers
cs.LG2024
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…
cs.DC2024
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…
cs.LG2024★ 7 cited
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…