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Found 7 papers, 0 papers with code
Hardware Aware Evolutionary Neural Architecture Search using Representation Similarity Metric
Our approach re-frames the neural architecture search problem as finding an architecture with performance similar to that of a reference model for a target hardware, while adhering to a cost constraint for that hardware.
Hardware Aware Neural Architecture Search
Neural Architecture Search
Impact of Disentanglement on Pruning Neural Networks
Deploying deep learning neural networks on edge devices, to accomplish task specific objectives in the real-world, requires a reduction in their memory footprint, power consumption, and latency.
A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects
The primary goal of this survey is to describe the current DL-based methods for spacecraft pose estimation in a comprehensive manner.
A Machine-Learning-Ready Dataset Prepared from the Solar and Heliospheric Observatory Mission
We present a Python tool to generate a standard dataset from solar images that allows for user-defined selection criteria and a range of pre-processing steps.
Ideas for Improving the Field of Machine Learning: Summarizing Discussion from the NeurIPS 2019 Retrospectives Workshop
This report documents ideas for improving the field of machine learning, which arose from discussions at the ML Retrospectives workshop at NeurIPS 2019.
Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties
Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient.
Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses
Breakthroughs in our understanding of physical phenomena have traditionally followed improvements in instrumentation.
