Search Results for author: Anahita Bhiwandiwalla

Found 6 papers, 1 papers with code

Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder

no code implementations4 Oct 2019 Kara Lamb, Garima Malhotra, Athanasios Vlontzos, Edward Wagstaff, Atılım Günes Baydin, Anahita Bhiwandiwalla, Yarin Gal, Alfredo Kalaitzis, Anthony Reina, Asti Bhatt

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

no code implementations3 Oct 2019 Kara Lamb, Garima Malhotra, Athanasios Vlontzos, Edward Wagstaff, Atılım Günes Baydin, Anahita Bhiwandiwalla, Yarin Gal, Alfredo Kalaitzis, Anthony Reina, Asti Bhatt

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.

BIG-bench Machine Learning

Using Scene Graph Context to Improve Image Generation

no code implementations11 Jan 2019 Subarna Tripathi, Anahita Bhiwandiwalla, Alexei Bastidas, Hanlin Tang

Generating realistic images from scene graphs asks neural networks to be able to reason about object relationships and compositionality.

Image Generation from Scene Graphs

Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning

1 code implementation24 Jan 2018 Scott Cyphers, Arjun K. Bansal, Anahita Bhiwandiwalla, Jayaram Bobba, Matthew Brookhart, Avijit Chakraborty, Will Constable, Christian Convey, Leona Cook, Omar Kanawi, Robert Kimball, Jason Knight, Nikolay Korovaiko, Varun Kumar, Yixing Lao, Christopher R. Lishka, Jaikrishnan Menon, Jennifer Myers, Sandeep Aswath Narayana, Adam Procter, Tristan J. Webb

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.

graph partitioning Management

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