2 papers
cs.LG2025
MLKV: Efficiently Scaling up Large Embedding Model Training with Disk-based Key-Value Storage
Yongjun He, Roger Waleffe, Zhichao Han +8
Many modern machine learning (ML) methods rely on embedding models to learn vector representations (embeddings) for a set of entities (embedding tables). As increasingly diverse ML…
cs.LG2024
Retrofitting Temporal Graph Neural Networks with Transformer
Qiang Huang, Xiao Yan, Xin Wang +5
Temporal graph neural networks (TGNNs) outperform regular GNNs by incorporating time information into graph-based operations. However, TGNNs adopt specialized models (e.g., TGN, TG…