activity
20162026
most citedEncoding Invariances in Deep Generative Models

20 citations · 119 across the 78 of their papers we have counts for

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Showing 2020Show all

8 papers · 1 filter

cs.CV2020

Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications

Luis G Riera, Matthew E. Carroll, Zhisheng Zhang +8

Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop…

cs.CE2020

Modeling electrochemical systems with weakly imposed Dirichlet boundary conditions

Sungu Kim, Makrand A. Khanwale, Robbyn K. Anand +1

Finite element modeling of charged species transport has enabled analysis, design, and optimization of a diverse array of electrochemical and electrokinetic devices. These systems…

math.NA2020

A fully-coupled framework for solving Cahn-Hilliard Navier-Stokes equations: Second-order, energy-stable numerical methods on adaptive octree based meshes

Makrand A Khanwale, Kumar Saurabh, Milinda Fernando +4

We present a fully-coupled, implicit-in-time framework for solving a thermodynamically-consistent Cahn-Hilliard Navier-Stokes system that models two-phase flows. In this work, we e…

math.NA2020

Industrial scale large eddy simulations (LES) with adaptive octree meshes using immersogeometric analysis

Kumar Saurabh, Boshun Gao, Milinda Fernando +7

We present a variant of the immersed boundary method integrated with octree meshes for highly efficient and accurate Large-Eddy Simulations (LES) of flows around complex geometries…

cs.LG2020

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models

Sergio Botelho, Ameya Joshi, Biswajit Khara +4

Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equation…

cs.CV2020★ 6 cited

Usefulness of interpretability methods to explain deep learning based plant stress phenotyping

Koushik Nagasubramanian, Asheesh K. Singh, Arti Singh +2

Deep learning techniques have been successfully deployed for automating plant stress identification and quantification. In recent years, there is a growing push towards training mo…