21 citations · 40 across the 9 of their papers we have counts for
7 papers · 1 filter
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks
Saket Gurukar, Shaileshh Bojja Venkatakrishnan, Balaraman Ravindran +1
Graph convolutional networks (GCNs) have achieved huge success in several machine learning (ML) tasks on graph-structured data. Recently, several sampling techniques have been prop…
Clustering Indices based Automatic Classification Model Selection
Sudarsun Santhiappan, Nitin Shravan, Balaraman Ravindran
Classification model selection is a process of identifying a suitable model class for a given classification task on a dataset. Traditionally, model selection is based on cross-val…
ReGrAt: Regularization in Graphs using Attention to handle class imbalance
Neeraja Kirtane, Jeshuren Chelladurai, Balaraman Ravindran +1
Node classification is an important task to solve in graph-based learning. Even though a lot of work has been done in this field, imbalance is neglected. Real-world data is not per…
GrabQC: Graph based Query Contextualization for automated ICD coding
Jeshuren Chelladurai, Sudarsun Santhiappan, Balaraman Ravindran
Automated medical coding is a process of codifying clinical notes to appropriate diagnosis and procedure codes automatically from the standard taxonomies such as ICD (International…
Multi-Variate Time Series Forecasting on Variable Subsets
Jatin Chauhan, Aravindan Raghuveer, Rishi Saket +2
We formulate a new inference task in the domain of multivariate time series forecasting (MTSF), called Variable Subset Forecast (VSF), where only a small subset of the variables is…
TAG: Task-based Accumulated Gradients for Lifelong learning
Pranshu Malviya, Balaraman Ravindran, Sarath Chandar
When an agent encounters a continual stream of new tasks in the lifelong learning setting, it leverages the knowledge it gained from the earlier tasks to help learn the new tasks b…
Fusion Graph Convolutional Networks
Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra +2
Semi-supervised node classification in attributed graphs, i.e., graphs with node features, involves learning to classify unlabeled nodes given a partially labeled graph. Label pred…