5 citations · 7 across the 6 of their papers we have counts for
6 papers
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…
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…
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…