Search Results for author:
Found 4 papers, 2 papers with code
A Package for Learning on Tabular and Text Data with Transformers
Recent progress in natural language processing has led to Transformer architectures becoming the predominant model used for natural language tasks.
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity
We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity.
Ranked #1 on
Graph Classification
on Web
Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction
We introduce Bi-GNN for modeling biological link prediction tasks such as drug-drug interaction (DDI) and protein-protein interaction (PPI).
Neural Maximum Common Subgraph Detection with Guided Subgraph Extraction
Maximum Common Subgraph (MCS) is defined as the largest subgraph that is commonly present in both graphs of a graph pair.
