no code implementations • 28 Dec 2022 • Tarun Narayanan, Ajay Krishnan, Anirudh Koul, Siddha Ganju
Applying Machine learning to domains like Earth Sciences is impeded by the lack of labeled data, despite a large corpus of raw data available in such domains.
no code implementations • 5 May 2022 • Jonathan Francis, Bingqing Chen, Siddha Ganju, Sidharth Kathpal, Jyotish Poonganam, Ayush Shivani, Vrushank Vyas, Sahika Genc, Ivan Zhukov, Max Kumskoy, Anirudh Koul, Jean Oh, Eric Nyberg
In the first stage of the challenge, we evaluate an autonomous agent's ability to drive as fast as possible, while adhering to safety constraints.
no code implementations • 20 Jan 2022 • Suhas Kotha, Anirudh Koul, Siddha Ganju, Meher Kasam
To solve this problem, we establish CELESTIAL-a self-supervised learning pipeline for effectively leveraging sparsely-labeled satellite imagery.
no code implementations • 10 Aug 2021 • Abhigya Sodani, Michael Levy, Anirudh Koul, Meher Anand Kasam, Siddha Ganju
Our similarity search system was created to be able to identify similar images from a potentially petabyte scale database that are similar to an input image, and for this we had to break down each query image into its features, which were generated by a classification layer stripped CNN trained in a supervised manner.
no code implementations • 7 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.
1 code implementation • 13 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.
1 code implementation • ICCV 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.
1 code implementation • 19 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.
no code implementations • 9 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.
1 code implementation • 1 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.