1 citations · 1 across the 4 of their papers we have counts for
4 papers
DS SERVE: A Framework for Efficient and Scalable Neural Retrieval
Jinjian Liu, Yichuan Wang, Xinxi Lyu +4
We present DS-Serve, a framework that transforms large-scale text datasets, comprising half a trillion tokens, into a high-performance neural retrieval system. DS-Serve offers both…
DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training
Renjie Liu, Yichuan Wang, Xiao Yan +5
Graph neural networks (GNNs) are machine learning models specialized for graph data and widely used in many applications. To train GNNs on large graphs that exceed CPU memory, seve…
MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy
Haitian Jiang, Renjie Liu, Zengfeng Huang +5
Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that…
NAS-based Recursive Stage Partial Network (RSPNet) for Light-Weight Semantic Segmentation
Yi-Chun Wang, Jun-Wei Hsieh, Ming-Ching Chang
Current NAS-based semantic segmentation methods focus on accuracy improvements rather than light-weight design. In this paper, we proposed a two-stage framework to design our NAS-b…