Search Results for author:
Found 3 papers, 2 papers with code
Stateful Large Language Model Serving with Pensieve
Large Language Models (LLMs) are wildly popular today and it is important to serve them efficiently.
Scalable Graph Neural Networks for Heterogeneous Graphs
Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data.
Ranked #20 on
Node Property Prediction
on ogbn-mag
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.
Ranked #35 on
Node Classification
on Cora
