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Found 4 papers, 2 papers with code
CELESTIAL: Classification Enabled via Labelless Embeddings with Self-supervised Telescope Image Analysis Learning
To solve this problem, we establish CELESTIAL-a self-supervised learning pipeline for effectively leveraging sparsely-labeled satellite imagery.
SpaceML: Distributed Open-source Research with Citizen Scientists for the Advancement of Space Technology for NASA
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
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
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.
