5 citations · 9 across the 8 of their papers we have counts for
8 papers
Scalable Unidirectional Pareto Optimality for Multi-Task Learning with Constraints
Soumyajit Gupta, Gurpreet Singh, Raghu Bollapragada +1
Multi-objective optimization (MOO) problems require balancing competing objectives, often under constraints. The Pareto optimal solution set defines all possible optimal trade-offs…
Tail-Net: Extracting Lowest Singular Triplets for Big Data Applications
Gurpreet Singh, Soumyajit Gupta
SVD serves as an exploratory tool in identifying the dominant features in the form of top rank-r singular factors corresponding to the largest singular values. For Big Data applica…
A Hybrid 2-stage Neural Optimization for Pareto Front Extraction
Gurpreet Singh, Soumyajit Gupta, Matthew Lease +1
Classification, recommendation, and ranking problems often involve competing goals with additional constraints (e.g., to satisfy fairness or diversity criteria). Such optimization…
SCA-Net: A Self-Correcting Two-Layer Autoencoder for Hyper-spectral Unmixing
Gurpreet Singh, Soumyajit Gupta, Clint Dawson
Hyperspectral unmixing involves separating a pixel as a weighted combination of its constituent endmembers and corresponding fractional abundances, with the current state of the ar…
Range-Net: A High Precision Streaming SVD for Big Data Applications
Gurpreet Singh, Soumyajit Gupta, Matthew Lease +1
In a Big Data setting computing the dominant SVD factors is restrictive due to the main memory requirements. Recently introduced streaming Randomized SVD schemes work under the res…
Extracting Optimal Solution Manifolds using Constrained Neural Optimization
Gurpreet Singh, Soumyajit Gupta, Matthew Lease
Constrained Optimization solution algorithms are restricted to point based solutions. In practice, single or multiple objectives must be satisfied, wherein both the objective funct…