72 citations · 98 across the 6 of their papers we have counts for
7 papers
PerfAgent: Profiler-Guided Iterative Refinement for Repository-Level Code Optimization
Ryan Deng, Yuanzhe Liu, Bastian Lipka +4
Large language model (LLM) agents now perform well on correctness-oriented repository-level tasks, including SWE-Bench issue resolution and feature implementation in real codebases…
The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset
Claudio Bellei, Muhua Xu, Ross Phillips +6
Subgraph representation learning is a technique for analyzing local structures (or shapes) within complex networks. Enabled by recent developments in scalable Graph Neural Networks…
Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching
Tim Kaler, Alexandros-Stavros Iliopoulos, Philip Murzynowski +3
Training and inference with graph neural networks (GNNs) on massive graphs has been actively studied since the inception of GNNs, owing to the widespread use and success of GNNs in…
Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining
Tim Kaler, Nickolas Stathas, Anne Ouyang +4
Improving the training and inference performance of graph neural networks (GNNs) is faced with a challenge uncommon in general neural networks: creating mini-batches requires a lot…
Cilkmem: Algorithms for Analyzing the Memory High-Water Mark of Fork-Join Parallel Programs
Tim Kaler, William Kuszmaul, Tao B. Schardl +1
Software engineers designing recursive fork-join programs destined to run on massively parallel computing systems must be cognizant of how their program's memory requirements scale…
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Aldo Pareja, Giacomo Domeniconi, Jie Chen +6
Graph representation learning resurges as a trending research subject owing to the widespread use of deep learning for Euclidean data, which inspire various creative designs of neu…