Search Results for author: Sundararajan Sellamanickam

Found 15 papers, 3 papers with code

A Piece-wise Polynomial Filtering Approach for Graph Neural Networks

1 code implementation7 Dec 2021 Vijay Lingam, Chanakya Ekbote, Manan Sharma, Rahul Ragesh, Arun Iyer, Sundararajan Sellamanickam

We study various aspects of our proposed model including, dependency on the number of eigencomponents utilized, latent polynomial filters learned, and performance of the individual polynomials on the node classification task.

Node Classification

Effective Polynomial Filter Adaptation for Graph Neural Networks

no code implementations29 Sep 2021 Vijay Lingam, Chanakya Ajit Ekbote, Manan Sharma, Rahul Ragesh, Arun Iyer, Sundararajan Sellamanickam

We study various aspects of our proposed model including, dependency on the number of eigencomponents utilized, latent polynomial filters learned, and performance of the individual polynomials on the node classification task.

Node Classification

IGLU: Efficient GCN Training via Lazy Updates

1 code implementation ICLR 2022 S Deepak Narayanan, Aditya Sinha, Prateek Jain, Purushottam Kar, Sundararajan Sellamanickam

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 graph.

User Embedding based Neighborhood Aggregation Method for Inductive Recommendation

no code implementations15 Feb 2021 Rahul Ragesh, Sundararajan Sellamanickam, Vijay Lingam, Arun Iyer, Ramakrishna Bairi

CF-LGCN-U models naturally possess the inductive capability for new items, and we propose a simple solution to generalize for new users.

Collaborative Filtering

HeteGCN: Heterogeneous Graph Convolutional Networks for Text Classification

no code implementations19 Aug 2020 Rahul Ragesh, Sundararajan Sellamanickam, Arun Iyer, Ram Bairi, Vijay Lingam

We consider the problem of learning efficient and inductive graph convolutional networks for text classification with a large number of examples and features.

General Classification Graph Embedding +2

A Graph Convolutional Network Composition Framework for Semi-supervised Classification

no code implementations8 Apr 2020 Rahul Ragesh, Sundararajan Sellamanickam, Vijay Lingam, Arun Iyer

Graph convolutional networks (GCNs) have gained popularity due to high performance achievable on several downstream tasks including node classification.

Classification General Classification +1

Learning Semantically Coherent and Reusable Kernels in Convolution Neural Nets for Sentence Classification

no code implementations1 Aug 2016 Madhusudan Lakshmana, Sundararajan Sellamanickam, Shirish Shevade, Keerthi Selvaraj

Motivated by this observation, we propose to learn kernels with semantic coherence using clustering scheme combined with Word2Vec representation and domain knowledge such as SentiWordNet.

Clustering General Classification +2

A Structured Prediction Approach for Missing Value Imputation

no code implementations9 Nov 2013 Rahul Kidambi, Vinod Nair, Sundararajan Sellamanickam, S. Sathiya Keerthi

In this paper we propose a structured output approach for missing value imputation that also incorporates domain constraints.

Imputation Structured Prediction

Large Margin Semi-supervised Structured Output Learning

no code implementations9 Nov 2013 P. Balamurugan, Shirish Shevade, Sundararajan Sellamanickam

The optimization problem, which in general is not convex, contains the loss terms associated with the labelled and unlabelled examples along with the domain constraints.

Structured Prediction

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