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Found 6 papers, 1 papers with code
Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks
This necessitates increased memory footprint and computational requirements for training.
Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder
High energy particles originating from solar activity travel along the the Earth's magnetic field and interact with the atmosphere around the higher latitudes.
Prediction of GNSS Phase Scintillations: A Machine Learning Approach
We propose a novel architecture and loss function to predict 1 hour in advance the magnitude of phase scintillations within a time window of plus-minus 5 minutes with state-of-the-art performance.
Heuristics for Image Generation from Scene Graphs
Existing scene graph to image models have two stages: (1) a scene composition stage, and an (2) image generation stage.
Using Scene Graph Context to Improve Image Generation
Generating realistic images from scene graphs asks neural networks to be able to reason about object relationships and compositionality.
Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning
The current approach, which we call "direct optimization", requires deep changes within each framework to improve the training performance for each hardware backend (CPUs, GPUs, FPGAs, ASICs) and requires $\mathcal{O}(fp)$ effort; where $f$ is the number of frameworks and $p$ is the number of platforms.
