Yizhou Sun

Entity Matching across Heterogeneous Sources

2015

Given an entity in a source domain, finding its matched entities from another (target) domain is an important task in many applications. Traditionally, the problem was usually addressed by first extracting major keywords corresponding to …

Motif-Driven Contrastive Learning of Graph Representations

2020 · arXiv (Cornell University)

Pre-training Graph Neural Networks (GNN) via self-supervised contrastive learning has recently drawn lots of attention. However, most existing works focus on node-level contrastive learning, which cannot capture global graph structure. The key challenge to conducting …

Differentiable Product Quantization for End-to-End Embedding Compression

2020 · International Conference on Machine Learning

Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings. Despite their effectiveness, the number of parameters in an embedding layer increases linearly with the number of …

PGE

2022 · Proceedings of the VLDB Endowment

Although product graphs (PGs) have gained increasing attentions in recent years for their successful applications in product search and recommendations, the extensive power of PGs can be limited by the inevitable involvement of various kinds …

ProgSG: Cross-Modality Representation Learning for Programs in Electronic Design Automation

2023 · arXiv (Cornell University)

Recent years have witnessed the growing popularity of domain-specific accelerators (DSAs), such as Google's TPUs, for accelerating various applications such as deep learning, search, autonomous driving, etc. To facilitate DSA designs, high-level synthesis (HLS) is …

Parameter-Efficient Tuning Large Language Models for Graph Representation Learning

2024 · arXiv (Cornell University)

Text-rich graphs, which exhibit rich textual information on nodes and edges, are prevalent across a wide range of real-world business applications. Large Language Models (LLMs) have demonstrated remarkable abilities in understanding text, which also introduced …

Heterogeneous Graph Transformer

2020

Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data. However, most GNNs are designed for homogeneous graphs, in which all nodes and edges belong to the same types, …