19 citations · 35 across the 8 of their papers we have counts for
8 papers
FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations
Chanakya Ekbote, Ajinkya Pankaj Deshpande, Arun Iyer +2
Unsupervised node representations learnt using contrastive learning-based methods have shown good performance on downstream tasks. However, these methods rely on augmentations that…
CodePlan: Repository-level Coding using LLMs and Planning
Ramakrishna Bairi, Atharv Sonwane, Aditya Kanade +6
Software engineering activities such as package migration, fixing errors reports from static analysis or testing, and adding type annotations or other specifications to a codebase,…
Landmarks and Regions: A Robust Approach to Data Extraction
Suresh Parthasarathy, Lincy Pattanaik, Anirudh Khatry +4
We propose a new approach to extracting data items or field values from semi-structured documents. Examples of such problems include extracting passenger name, departure time and d…
A Piece-wise Polynomial Filtering Approach for Graph Neural Networks
Vijay Lingam, Chanakya Ekbote, Manan Sharma +3
Graph Neural Networks (GNNs) exploit signals from node features and the input graph topology to improve node classification task performance. However, these models tend to perform…
Jigsaw: Large Language Models meet Program Synthesis
Naman Jain, Skanda Vaidyanath, Arun Iyer +4
Large pre-trained language models such as GPT-3, Codex, and Google's language model are now capable of generating code from natural language specifications of programmer intent. We…
Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs
Vijay Lingam, Rahul Ragesh, Arun Iyer +1
Graph Neural Networks (GNNs) have shown excellent performance on graphs that exhibit strong homophily with respect to the node labels i.e. connected nodes have same labels. However…
GLAM: Graph Learning by Modeling Affinity to Labeled Nodes for Graph Neural Networks
Vijay Lingam, Arun Iyer, Rahul Ragesh
Graph Neural Networks have shown excellent performance on semi-supervised classification tasks. However, they assume access to a graph that may not be often available in practice.…
HeteGCN: Heterogeneous Graph Convolutional Networks for Text Classification
Rahul Ragesh, Sundararajan Sellamanickam, Arun Iyer +2
We consider the problem of learning efficient and inductive graph convolutional networks for text classification with a large number of examples and features. Existing state-of-the…