Search Results for author:
Found 12 papers, 1 papers with code
Laplacian-guided Entropy Model in Neural Codec with Blur-dissipated Synthesis
The global spatial context is built upon the Transformer, which is specifically designed for image compression tasks.
Neural-based Compression Scheme for Solar Image Data
In this work, we propose an adversarially trained neural network, equipped with local and non-local attention modules to capture both the local and global structure of the image resulting in a better trade-off in rate-distortion (RD) compared to conventional hand-engineered codecs.
Multi-Context Dual Hyper-Prior Neural Image Compression
In addition, we introduce a novel entropy model that incorporates two different hyperpriors to model cross-channel and spatial dependencies of the latent representation.
Multi-spectral Entropy Constrained Neural Compression of Solar Imagery
Recently successful end-to-end optimized neural network-based image compression systems have shown great potential to be used in an ad-hoc manner.
Context-Aware Neural Video Compression on Solar Dynamics Observatory
NASA's Solar Dynamics Observatory (SDO) mission collects large data volumes of the Sun's daily activity.
Probabilistic Solar Proxy Forecasting with Neural Network Ensembles
The best models found in this work are ensemble approaches using multi step or a combination of multi step and dynamic predictions.
Reduced Order Probabilistic Emulation for Physics-Based Thermosphere Models
The geospace environment is volatile and highly driven.
Attention-Based Generative Neural Image Compression on Solar Dynamics Observatory
We have designed an ad-hoc ANN-based image compression scheme to reduce the amount of data needed to be stored and retrieved on space missions studying solar dynamics.
Calibrated and Enhanced NRLMSIS 2.0 Model with Uncertainty Quantification
In this work, we develop an exospheric temperature model based in machine learning (ML) that can be used with NRLMSIS 2. 0 to calibrate it relative to high-fidelity satellite density estimates.
Science through Machine Learning: Quantification of Poststorm Thermospheric Cooling
Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight.
Uncertainty Quantification Techniques for Space Weather Modeling: Thermospheric Density Application
For the global model regressed on the SET HASDM density database, we achieve errors of 11% on independent test data with well-calibrated uncertainty estimates.
Machine-Learned HASDM Model with Uncertainty Quantification
These models leverage Monte Carlo (MC) dropout to provide uncertainty estimates, and the use of the NLPD loss function results in well-calibrated uncertainty estimates without sacrificing model accuracy (<10% mean absolute error).
