189 citations · 243 across the 21 of their papers we have counts for
21 papers
A Generalization of Continuous Relaxation in Structured Pruning
Brad Larson, Bishal Upadhyaya, Luke McDermott +1
Deep learning harnesses massive parallel floating-point processing to train and evaluate large neural networks. Trends indicate that deeper and larger neural networks with an incre…
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping
Siddha Ganju, Amartya Hatua, Peter Jenniskens +3
The Cameras for Allsky Meteor Surveillance (CAMS) project, funded by NASA starting in 2010, aims to map our meteor showers by triangulating meteor trajectories detected in low-ligh…
Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning
Tarun Narayanan, Ajay Krishnan, Anirudh Koul +1
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, trainin…
Deep learning based landslide density estimation on SAR data for rapid response
Vanessa Boehm, Wei Ji Leong, Ragini Bal Mahesh +5
This work aims to produce landslide density estimates using Synthetic Aperture Radar (SAR) satellite imageries to prioritise emergency resources for rapid response. We use the Unit…
SAR-based landslide classification pretraining leads to better segmentation
Vanessa Böhm, Wei Ji Leong, Ragini Bal Mahesh +5
Rapid assessment after a natural disaster is key for prioritizing emergency resources. In the case of landslides, rapid assessment involves determining the extent of the area affec…
Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes
Vanessa Boehm, Wei Ji Leong, Ragini Bal Mahesh +5
With climate change predicted to increase the likelihood of landslide events, there is a growing need for rapid landslide detection technologies that help inform emergency response…
Global geomagnetic perturbation forecasting using Deep Learning
Vishal Upendran, Panagiotis Tigas, Banafsheh Ferdousi +6
Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field which arise from the interaction of the solar wind with Earth's magnetosphere,…
Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing
Jonathan Francis, Bingqing Chen, Siddha Ganju +10
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…
CELESTIAL: Classification Enabled via Labelless Embeddings with Self-supervised Telescope Image Analysis Learning
Suhas Kotha, Anirudh Koul, Siddha Ganju +1
A common class of problems in remote sensing is scene classification, a fundamentally important task for natural hazards identification, geographic image retrieval, and environment…
Scalable Reverse Image Search Engine for NASAWorldview
Abhigya Sodani, Michael Levy, Anirudh Koul +2
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…
Reducing Effects of Swath Gaps on Unsupervised Machine Learning Models for NASA MODIS Instruments
Sarah Chen, Esther Cao, Anirudh Koul +3
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 int…
Scalable Data Balancing for Unlabeled Satellite Imagery
Deep Patel, Erin Gao, Anirudh Koul +2
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 c…
Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning
Sayak Paul, Siddha Ganju
Floods wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems and economies. The NASA Impact Flood Detection competition ta…
Next-Gen Machine Learning Supported Diagnostic Systems for Spacecraft
Athanasios Vlontzos, Gabriel Sutherland, Siddha Ganju +1
Future short or long-term space missions require a new generation of monitoring and diagnostic systems due to communication impasses as well as limitations in specialized crew and…
Learn-to-Race: A Multimodal Control Environment for Autonomous Racing
James Herman, Jonathan Francis, Siddha Ganju +7
Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing. At…
Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition
Panagiotis Tigas, Téo Bloch, Vishal Upendran +6
Modeling and forecasting the solar wind-driven global magnetic field perturbations is an open challenge. Current approaches depend on simulations of computationally demanding model…
Technology Readiness Levels for Machine Learning Systems
Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic +12
The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of dilig…
SpaceML: Distributed Open-source Research with Citizen Scientists for the Advancement of Space Technology for NASA
Anirudh Koul, Siddha Ganju, Meher Kasam +1
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 wo…
Learnings from Frontier Development Lab and SpaceML -- AI Accelerators for NASA and ESA
Siddha Ganju, Anirudh Koul, Alexander Lavin +3
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 resear…
Technology Readiness Levels for AI & ML
Alexander Lavin, Gregory Renard
The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence…
What's in a Question: Using Visual Questions as a Form of Supervision
Siddha Ganju, Olga Russakovsky, Abhinav Gupta
Collecting fully annotated image datasets is challenging and expensive. Many types of weak supervision have been explored: weak manual annotations, web search results, temporal con…