2 citations · 2 across the 5 of their papers we have counts for
9 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.…
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…
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…
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…