activity
20172020
most citedEncoding Invariances in Deep Generative Models

20 citations · 24 across the 4 of their papers we have counts for

collaborators

5 papers

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.LG20193 cited

Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data

John Just, Sambuddha Ghosal

Advances in deep generative and density models have shown impressive capacity to model complex probability density functions in lower-dimensional space. Also, applying such models…

cs.LG201920 cited

Encoding Invariances in Deep Generative Models

Viraj Shah, Ameya Joshi, Sambuddha Ghosal +4

Reliable training of generative adversarial networks (GANs) typically require massive datasets in order to model complicated distributions. However, in several applications, traini…

cond-mat.soft2019

Anomalous diffusion in an electrolyte saturated paper matrix

Sankha Shuvra Das, Sumeet Kumar, Sambuddha Ghosal +2

Diffusion of colored dye on water saturated paper substrates has been traditionally exploited with great skill by renowned water color artists. The same physics finds more recent p…

stat.ML20171 cited

Interpretable Deep Learning applied to Plant Stress Phenotyping

Sambuddha Ghosal, David Blystone, Asheesh K. Singh +3

Availability of an explainable deep learning model that can be applied to practical real world scenarios and in turn, can consistently, rapidly and accurately identify specific and…