George Karypis · PaperMetrix

DGL-KE: Training Knowledge Graph Embeddings at Scale

2020 · arXiv (Cornell University)

Knowledge graphs have emerged as a key abstraction for organizing information in diverse domains and their embeddings are increasingly used to harness their information in various information retrieval and machine learning tasks. However, the ever …

Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

2019 · arXiv (Cornell University)

Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs. In this paper, we present the design principles and implementation of Deep Graph Library (DGL). DGL …

Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation

2021 · arXiv (Cornell University)

Consistency training is a popular method to improve deep learning models in computer vision and natural language processing. Graph neural networks (GNNs) have achieved remarkable performance in a variety of network science learning tasks, but …

Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger

2022 · arXiv (Cornell University)

Per-example gradient clipping is a key algorithmic step that enables practical differential private (DP) training for deep learning models. The choice of clipping threshold R, however, is vital for achieving high accuracy under DP. We …

OpenTab: Advancing Large Language Models as Open-domain Table Reasoners

2024 · arXiv (Cornell University)

Large Language Models (LLMs) trained on large volumes of data excel at various natural language tasks, but they cannot handle tasks requiring knowledge that has not been trained on previously. One solution is to use …

Parameter-Efficient Tuning Large Language Models for Graph Representation Learning