4 citations · 8 across the 5 of their papers we have counts for
9 papers
xEM: Explainable Entity Matching in Customer 360
Sukriti Jaitly, Deepa Mariam George, Balaji Ganesan +2
Entity matching in Customer 360 is the task of determining if multiple records represent the same real world entity. Entities are typically people, organizations, locations, and ev…
Reimagining GNN Explanations with ideas from Tabular Data
Anjali Singh, Shamanth R Nayak K, Balaji Ganesan
Explainability techniques for Graph Neural Networks still have a long way to go compared to explanations available for both neural and decision decision tree-based models trained o…
Towards Automated Evaluation of Explanations in Graph Neural Networks
Vanya BK, Balaji Ganesan, Aniket Saxena +2
Explaining Graph Neural Networks predictions to end users of AI applications in easily understandable terms remains an unsolved problem. In particular, we do not have well develope…
Explainable Link Prediction for Privacy-Preserving Contact Tracing
Balaji Ganesan, Hima Patel, Sameep Mehta
Contact Tracing has been used to identify people who were in close proximity to those infected with SARS-Cov2 coronavirus. A number of digital contract tracing applications have be…
Link Prediction using Graph Neural Networks for Master Data Management
Balaji Ganesan, Srinivas Parkala, Neeraj R Singh +5
Learning graph representations of n-ary relational data has a number of real world applications like anti-money laundering, fraud detection, and customer due diligence. Contact tra…
Data Augmentation for Personal Knowledge Base Population
Lingraj S Vannur, Balaji Ganesan, Lokesh Nagalapatti +2
Cold start knowledge base population (KBP) is the problem of populating a knowledge base from unstructured documents. While artificial neural networks have led to significant impro…