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Found 21 papers, 9 papers with code
A Generalization of Continuous Relaxation in Structured Pruning
Structured pruning asserts that while large networks enable us to find solutions to complex computer vision problems, a smaller, computationally efficient sub-network can be derived from the large neural network that retains model accuracy but significantly improves computational efficiency.
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping
Our research aimed to streamline the data processing by implementing an automated cloud-based AI-enabled pipeline and improve the data visualization to improve the rate of discoveries by involving the public in monitoring the meteor detections.
Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning
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.
Deep learning based landslide density estimation on SAR data for rapid response
Since such data might not be available during other events or regions, we aimed to produce a landslide density map using only elevation and SAR data to be useful to decision-makers in rapid response scenarios.
SAR-based landslide classification pretraining leads to better segmentation
In the case of landslides, rapid assessment involves determining the extent of the area affected and measuring the size and location of individual landslides.
Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes
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 responses.
Global geomagnetic perturbation forecasting using Deep Learning
The model summarizes 2 hours of solar wind measurement using a Gated Recurrent Unit, and generates forecasts of coefficients which are folded with a spherical harmonic basis to enable global forecasts.
Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing
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.
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.
Scalable Reverse Image Search Engine for NASAWorldview
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.
Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning
Floods wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems and economies.
Scalable Data Balancing for Unlabeled Satellite Imagery
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
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.
Next-Gen Machine Learning Supported Diagnostic Systems for Spacecraft
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 equipment.
Learn-to-Race: A Multimodal Control Environment for Autonomous Racing
Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing.
Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition
Modeling and forecasting the solar wind-driven global magnetic field perturbations is an open challenge.
Technology Readiness Levels for Machine Learning Systems
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.
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.
What's in a Question: Using Visual Questions as a Form of Supervision
For instance, the question "what is the breed of the dog?"
