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Baskar Ganapathysubramanian

Researcher at Iowa State University

Publications -  262
Citations -  6876

Baskar Ganapathysubramanian is an academic researcher from Iowa State University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 34, co-authored 221 publications receiving 4808 citations. Previous affiliations of Baskar Ganapathysubramanian include Cornell University.

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Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications.

TL;DR: In this article, a multiview image-based yield estimation framework utilizing deep learning architectures was developed for soybean genotype seed yield rank prediction from in-field video data collected by a ground robot.
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Parallel framework for dimensionality reduction of large-scale datasets

TL;DR: This paper identifies key components underlying the spectral dimensionality reduction techniques, and proposes their efficient parallel implementation, and shows that the resulting framework can be used to process datasets consisting of millions of points when executed on a 16,000-core cluster, which is beyond the reach of currently available methods.
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Simulating two-phase flows with thermodynamically consistent energy stable Cahn-Hilliard Navier-Stokes equations on parallel adaptive octree based meshes

TL;DR: Simulation of two-phase flows with deforming interfaces at various density contrasts by solving thermodynamically consistent Cahn-Hilliard Navier-Stokes equations using an unconditionally energy-stable Crank-Nicolson-type time integration scheme is reported on.
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Tuning domain size and crystallinity in isoindigo/PCBM organic solar cells via solution shearing

TL;DR: In this article, an experimental investigation of a low bandgap polymer/fullerene system, poly-isoindigo thienothiophene/PC61BM, using a lab-scale analogue to roll-to-roll coating as the fabrication tool in order to understand the impact of processing parameters on morphological evolution is presented.
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How useful is Active Learning for Image-based Plant Phenotyping?

TL;DR: It is observed that the classification performance of deep learning models using active learning based acquisition strategies is better than random sampling‐based acquisition for two vastly different image‐based classification datasets.