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Found 3 papers, 2 papers with code
Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiation
We provide factual evidence that prior work did not thoroughly examine the impact of natural hazards on ML models meant for spacecraft.
Distilled Gradual Pruning with Pruned Fine-tuning
Neural Networks (NNs) have been driving machine learning progress in recent years, but their larger models present challenges in resource-limited environments.
Ranked #25 on
Image Classification
on CIFAR-10
(Top-1 Accuracy metric)
Training binary neural networks without floating point precision
The main goal of this work is to improve the efficiency of training binary neural networks, which are low latency and low energy networks.
