Search Results for author:
Found 4 papers, 3 papers with code
Does Negative Sampling Matter? A Review with Insights into its Theory and Applications
Is there a general framework that can incorporate all existing negative sampling methods?
BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs
To make each mini-batch have fewer false negatives, we design the proximity graph of randomly-selected instances.
GRAND+: Scalable Graph Random Neural Networks
In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning.
Ranked #1 on
Node Classification
on MAG-scholar-C
Learning Intents behind Interactions with Knowledge Graph for Recommendation
In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN).
Cannot find the paper you are looking for? You can
Submit a new open access paper.
