136 citations · 466 across the 24 of their papers we have counts for
5 papers · 1 filter
LLM Augmented LLMs: Expanding Capabilities through Composition
Rachit Bansal, Bidisha Samanta, Siddharth Dalmia +6
Foundational models with billions of parameters which have been trained on large corpora of data have demonstrated non-trivial skills in a variety of domains. However, due to their…
Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network
Justin Lovelace, Denis Newman-Griffis, Shikhar Vashishth +2
Knowledge Graph (KG) completion research usually focuses on densely connected benchmark datasets that are not representative of real KGs. We curate two KG datasets that include bio…
Composition-based Multi-Relational Graph Convolutional Networks
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin +1
Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in modeling graph-structured data. However, the primary focus has been on handling simple undire…
InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin +2
Most existing knowledge graphs suffer from incompleteness, which can be alleviated by inferring missing links based on known facts. One popular way to accomplish this is to generat…
Confidence-based Graph Convolutional Networks for Semi-Supervised Learning
Shikhar Vashishth, Prateek Yadav, Manik Bhandari +1
Predicting properties of nodes in a graph is an important problem with applications in a variety of domains. Graph-based Semi-Supervised Learning (SSL) methods aim to address this…