16 citations · 26 across the 11 of their papers we have counts for
13 papers
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction
Hansheng Xue, Vaibhav Rajan, Yu Lin
Understanding genetic variation, e.g., through mutations, in organisms is crucial to unravel their effects on the environment and human health. A fundamental characterization can b…
ExpertNet: A Symbiosis of Classification and Clustering
Shivin Srivastava, Kenji Kawaguchi, Vaibhav Rajan
A widely used paradigm to improve the generalization performance of high-capacity neural models is through the addition of auxiliary unsupervised tasks during supervised training.…
RepBin: Constraint-based Graph Representation Learning for Metagenomic Binning
Hansheng Xue, Vijini Mallawaarachchi, Yujia Zhang +2
Mixed communities of organisms are found in many environments (from the human gut to marine ecosystems) and can have profound impact on human health and the environment. Metagenomi…
Multi-way Clustering and Discordance Analysis through Deep Collective Matrix Tri-Factorization
Ragunathan Mariappan, Vaibhav Rajan
Heterogeneous multi-typed, multimodal relational data is increasingly available in many domains and their exploratory analysis poses several challenges. We advance the state-of-the…
Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks
Hansheng Xue, Luwei Yang, Vaibhav Rajan +3
A bipartite network is a graph structure where nodes are from two distinct domains and only inter-domain interactions exist as edges. A large number of network embedding methods ex…
Clustering Aware Classification for Risk Prediction and Subtyping in Clinical Data
Shivin Srivastava, Siddharth Bhatia, Lingxiao Huang +3
In data containing heterogeneous subpopulations, classification performance benefits from incorporating the knowledge of cluster structure in the classifier. Previous methods for s…
Improved Inference of Gaussian Mixture Copula Model for Clustering and Reproducibility Analysis using Automatic Differentiation
Siva Rajesh Kasa, Vaibhav Rajan
Copulas provide a modular parameterization of multivariate distributions that decouples the modeling of marginals from the dependencies between them. Gaussian Mixture Copula Model…
Multi-way Spectral Clustering of Augmented Multi-view Data through Deep Collective Matrix Tri-factorization
Ragunathan Mariappan, Siva Rajesh Kasa, Vaibhav Rajan
We present the first deep learning based architecture for collective matrix tri-factorization (DCMTF) of arbitrary collections of matrices, also known as augmented multi-view data.…
Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional Data
Siva Rajesh Kasa, Vaibhav Rajan
We study two practically important cases of model based clustering using Gaussian Mixture Models: (1) when there is misspecification and (2) on high dimensional data, in the light…
Automatic Differentiation in Mixture Models
Siva Rajesh Kasa, Vaibhav Rajan
In this article, we discuss two specific classes of models - Gaussian Mixture Copula models and Mixture of Factor Analyzers - and the advantages of doing inference with gradient de…
Inferring Concept Prerequisite Relations from Online Educational Resources
Sudeshna Roy, Meghana Madhyastha, Sheril Lawrence +1
The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large…
Deep Collective Matrix Factorization for Augmented Multi-View Learning
Ragunathan Mariappan, Vaibhav Rajan
Learning by integrating multiple heterogeneous data sources is a common requirement in many tasks. Collective Matrix Factorization (CMF) is a technique to learn shared latent repre…
A Statistical Model for Stroke Outcome Prediction and Treatment Planning
Abhishek Sengupta, Vaibhav Rajan, Sakyajit Bhattacharya +1
Stroke is a major cause of mortality and long--term disability in the world. Predictive outcome models in stroke are valuable for personalized treatment, rehabilitation planning an…