Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Koul, Anirudh"'
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. For instance, training a wildfire classifier on satellite imagery requires curating a massi
Externí odkaz:
http://arxiv.org/abs/2212.14099
Autor:
Francis, Jonathan, Chen, Bingqing, Ganju, Siddha, Kathpal, Sidharth, Poonganam, Jyotish, Shivani, Ayush, Vyas, Vrushank, Genc, Sahika, Zhukov, Ivan, Kumskoy, Max, Koul, Anirudh, Oh, Jean, Nyberg, Eric
We present the results of our autonomous racing virtual challenge, based on the newly-released Learn-to-Race (L2R) simulation framework, which seeks to encourage interdisciplinary research in autonomous driving and to help advance the state of the ar
Externí odkaz:
http://arxiv.org/abs/2205.02953
A common class of problems in remote sensing is scene classification, a fundamentally important task for natural hazards identification, geographic image retrieval, and environment monitoring. Recent developments in this field rely label-dependent su
Externí odkaz:
http://arxiv.org/abs/2201.08001
Researchers often spend weeks sifting through decades of unlabeled satellite imagery(on NASA Worldview) in order to develop datasets on which they can start conducting research. We developed an interactive, scalable and fast image similarity search e
Externí odkaz:
http://arxiv.org/abs/2108.04479
Data imbalance is a ubiquitous problem in machine learning. In large scale collected and annotated datasets, data imbalance is either mitigated manually by undersampling frequent classes and oversampling rare classes, or planned for with imputation a
Externí odkaz:
http://arxiv.org/abs/2107.03227
Autor:
Chen, Sarah, Cao, Esther, Koul, Anirudh, Ganju, Siddha, Praveen, Satyarth, Kasam, Meher Anand
Due to the nature of their pathways, NASA Terra and NASA Aqua satellites capture imagery containing swath gaps, which are areas of no data. Swath gaps can overlap the region of interest (ROI) completely, often rendering the entire imagery unusable by
Externí odkaz:
http://arxiv.org/abs/2106.07113
Autor:
Herman, James, Francis, Jonathan, Ganju, Siddha, Chen, Bingqing, Koul, Anirudh, Gupta, Abhinav, Skabelkin, Alexey, Zhukov, Ivan, Kumskoy, Max, Nyberg, Eric
Publikováno v:
International Conference on Computer Vision (ICCV), 2021
Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing. At the same time, existing racing simulation frameworks struggle in captu
Externí odkaz:
http://arxiv.org/abs/2103.11575
Traditionally, academic labs conduct open-ended research with the primary focus on discoveries with long-term value, rather than direct products that can be deployed in the real world. On the other hand, research in the industry is driven by its expe
Externí odkaz:
http://arxiv.org/abs/2012.10610
Autor:
Ganju, Siddha, Koul, Anirudh, Lavin, Alexander, Veitch-Michaelis, Josh, Kasam, Meher, Parr, James
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 ind
Externí odkaz:
http://arxiv.org/abs/2011.04776
Autor:
Khurana, Udayan, Koul, Anirudh
In this paper, a new compression scheme for text is presented. The same is efficient in giving high compression ratios and enables super fast searching within the compressed text. Typical compression ratios of 70-80% and reducing the search time by 8
Externí odkaz:
http://arxiv.org/abs/cs/0505056