Search Results for author: Anirudh Koul

Found 6 papers, 3 papers with code

Scalable Data Balancing for Unlabeled Satellite Imagery

no code implementations7 Jul 2021 Deep Patel, Erin Gao, Anirudh Koul, Siddha Ganju, Meher Anand Kasam

Collecting fully annotated datasets is challenging, especially for large scale satellite systems such as the unlabeled NASA's 35 PB Earth Imagery dataset.


Reducing Effects of Swath Gaps on Unsupervised Machine Learning Models for NASA MODIS Instruments

1 code implementation13 Jun 2021 Sarah Chen, Esther Cao, Anirudh Koul, Siddha Ganju, Satyarth Praveen, Meher Anand Kasam

We compare the model trained with our augmentation techniques on the swath gap-filled data with the model trained on the original swath gap-less data and note highly augmented performance.

Learn-to-Race: A Multimodal Control Environment for Autonomous Racing

1 code implementation22 Mar 2021 James Herman, Jonathan Francis, Siddha Ganju, Bingqing Chen, Anirudh Koul, Abhinav Gupta, Alexey Skabelkin, Ivan Zhukov, Max Kumskoy, Eric Nyberg

Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing.

Autonomous Driving Trajectory Prediction

SpaceML: Distributed Open-source Research with Citizen Scientists for the Advancement of Space Technology for NASA

no code implementations19 Dec 2020 Anirudh Koul, Siddha Ganju, Meher Kasam, James Parr

With research organizations focused on an exploding array of fields and resources spread thin, opportunities for the maturation of interdisciplinary research reduce.

Learnings from Frontier Development Lab and SpaceML -- AI Accelerators for NASA and ESA

no code implementations9 Nov 2020 Siddha Ganju, Anirudh Koul, Alexander Lavin, Josh Veitch-Michaelis, Meher Kasam, James Parr

Research with AI and ML technologies lives in a variety of settings with often asynchronous goals and timelines: academic labs and government organizations pursue open-ended research focusing on discoveries with long-term value, while research in industry is driven by commercial pursuits and hence focuses on short-term timelines and return on investment.

Practical Deep Learning for Cloud, Mobile, and Edge

1 code implementation1 Oct 2019 Anirudh Koul, Siddha Ganju, Meher Kasam

Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin.

Transfer Learning

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