Qinghao Hu

activity

20152026

most citedFrom Hashing to CNNs: Training BinaryWeight Networks via Hashing

39 citations · 118 across the 21 of their papers we have counts for

collaborators

Showing cs.LGShow all

3 papers · 1 filter

cs.LG2026

MODE: Modality-Decomposed Expert-Level Mixed-Precision Quantization for MoE Multimodal LLMs

Yuanteng Chen, Nanxin Zeng, Peisong Wang +9

Mixture-of-Experts Multimodal Large Language Models (MoE-MLLMs) offer remarkable performance but incur prohibitive GPU memory costs, making compression essential. Among PTQ methods…

cs.LG2024★ 7 cited

FastGL: A GPU-Efficient Framework for Accelerating Sampling-Based GNN Training at Large Scale

Zeyu Zhu, Peisong Wang, Qinghao Hu +3

Graph Neural Networks (GNNs) have shown great superiority on non-Euclidean graph data, achieving ground-breaking performance on various graph-related tasks. As a practical solution…

cs.LG2023★ 1 cited

$A^{2} Q$ : Aggregation-Aware Quantization for Graph Neural Networks

Zeyu Zhu, Fanrong Li, Zitao Mo +5

As graph data size increases, the vast latency and memory consumption during inference pose a significant challenge to the real-world deployment of Graph Neural Networks (GNNs). Wh…

MODE: Modality-Decomposed Expert-Level Mixed-Precision Quantization for MoE Multimodal LLMs

FastGL: A GPU-Efficient Framework for Accelerating Sampling-Based GNN Training at Large Scale

A2Q: Aggregation-Aware Quantization for Graph Neural Networks

$A^{2} Q$ : Aggregation-Aware Quantization for Graph Neural Networks