5 citations · 8 across the 5 of their papers we have counts for
5 papers
Haystack Engineering: Context Engineering for Heterogeneous and Agentic Long-Context Evaluation
Mufei Li, Dongqi Fu, Limei Wang +10
Modern long-context large language models (LLMs) perform well on synthetic "needle-in-a-haystack" (NIAH) benchmarks, but such tests overlook how noisy contexts arise from biased re…
A Scalable and Effective Alternative to Graph Transformers
Kaan Sancak, Zhigang Hua, Jin Fang +5
Graph Neural Networks (GNNs) have shown impressive performance in graph representation learning, but they face challenges in capturing long-range dependencies due to their limited…
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
Dongqi Fu, Zhigang Hua, Yan Xie +7
Graph transformer has been proven as an effective graph learning method for its adoption of attention mechanism that is capable of capturing expressive representations from complex…
MG-GCN: Scalable Multi-GPU GCN Training Framework
Muhammed Fatih Balın, Kaan Sancak, Ümit V. Çatalyürek
Full batch training of Graph Convolutional Network (GCN) models is not feasible on a single GPU for large graphs containing tens of millions of vertices or more. Recent work has sh…
On Symmetric Rectilinear Matrix Partitioning
Abdurrahman Yaşar, Muhammed Fatih Balin, Xiaojing An +2
Even distribution of irregular workload to processing units is crucial for efficient parallelization in many applications. In this work, we are concerned with a spatial partitionin…