19 citations · 47 across the 19 of their papers we have counts for
19 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…
Simulating Network Paths with Recurrent Buffering Units
Divyam Anshumaan, Sriram Balasubramanian, Shubham Tiwari +3
Simulating physical network paths (e.g., Internet) is a cornerstone research problem in the emerging sub-field of AI-for-networking. We seek a model that generates end-to-end packe…
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…
IGLU: Efficient GCN Training via Lazy Updates
S Deepak Narayanan, Aditya Sinha, Prateek Jain +2
Training multi-layer Graph Convolution Networks (GCN) using standard SGD techniques scales poorly as each descent step ends up updating node embeddings for a large portion of the g…
Effective Eigendecomposition based Graph Adaptation for Heterophilic Networks
Vijay Lingam, Rahul Ragesh, Arun Iyer +1
Graph Neural Networks (GNNs) exhibit excellent performance when graphs have strong homophily property, i.e. connected nodes have the same labels. However, they perform poorly on he…
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…
User Embedding based Neighborhood Aggregation Method for Inductive Recommendation
Rahul Ragesh, Sundararajan Sellamanickam, Vijay Lingam +2
We consider the problem of learning latent features (aka embedding) for users and items in a recommendation setting. Given only a user-item interaction graph, the goal is to recomm…
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…
A Graph Convolutional Network Composition Framework for Semi-supervised Classification
Rahul Ragesh, Sundararajan Sellamanickam, Vijay Lingam +1
Graph convolutional networks (GCNs) have gained popularity due to high performance achievable on several downstream tasks including node classification. Several architectural varia…
Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles
Dhruv Mahajan, Vivek Gupta, S Sathiya Keerthi +3
For many applications, an ensemble of base classifiers is an effective solution. The tuning of its parameters(number of classes, amount of data on which each classifier is to be tr…
Learning Semantically Coherent and Reusable Kernels in Convolution Neural Nets for Sentence Classification
Madhusudan Lakshmana, Sundararajan Sellamanickam, Shirish Shevade +1
The state-of-the-art CNN models give good performance on sentence classification tasks. The purpose of this work is to empirically study desirable properties such as semantic coher…
A Quantitative Evaluation Framework for Missing Value Imputation Algorithms
Vinod Nair, Rahul Kidambi, Sundararajan Sellamanickam +3
We consider the problem of quantitatively evaluating missing value imputation algorithms. Given a dataset with missing values and a choice of several imputation algorithms to fill…
Large Margin Semi-supervised Structured Output Learning
P. Balamurugan, Shirish Shevade, Sundararajan Sellamanickam
In structured output learning, obtaining labelled data for real-world applications is usually costly, while unlabelled examples are available in abundance. Semi-supervised structur…
A Structured Prediction Approach for Missing Value Imputation
Rahul Kidambi, Vinod Nair, Sundararajan Sellamanickam +1
Missing value imputation is an important practical problem. There is a large body of work on it, but there does not exist any work that formulates the problem in a structured outpu…
Extension of TSVM to Multi-Class and Hierarchical Text Classification Problems With General Losses
Sathiya Keerthi Selvaraj, Sundararajan Sellamanickam, Shirish Shevade
Transductive SVM (TSVM) is a well known semi-supervised large margin learning method for binary text classification. In this paper we extend this method to multi-class and hierarch…
Predictive Approaches For Gaussian Process Classifier Model Selection
Sundararajan Sellamanickam, Sathiya Keerthi Selvaraj
In this paper we consider the problem of Gaussian process classifier (GPC) model selection with different Leave-One-Out (LOO) Cross Validation (CV) based optimization criteria and…
An Additive Model View to Sparse Gaussian Process Classifier Design
Sundararajan Sellamanickam, Shirish Shevade
We consider the problem of designing a sparse Gaussian process classifier (SGPC) that generalizes well. Viewing SGPC design as constructing an additive model like in boosting, we p…
Transductive Classification Methods for Mixed Graphs
Sundararajan Sellamanickam, Sathiya Keerthi Selvaraj
In this paper we provide a principled approach to solve a transductive classification problem involving a similar graph (edges tend to connect nodes with same labels) and a dissimi…
Graph Based Classification Methods Using Inaccurate External Classifier Information
Sundararajan Sellamanickam, Sathiya Keerthi Selvaraj
In this paper we consider the problem of collectively classifying entities where relational information is available across the entities. In practice inaccurate class distribution…