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Found 4 papers, 2 papers with code
To Raise or Not To Raise: The Autonomous Learning Rate Question
There is a parameter ubiquitous throughout the deep learning world: learning rate.
Optimizing Data Augmentation Policy Through Random Unidimensional Search
It is no secret amongst deep learning researchers that finding the optimal data augmentation strategy during training can mean the difference between state-of-the-art performance and a run-of-the-mill result.
Impact of Inference Accelerators on hardware selection
As opportunities for AI-assisted healthcare grow steadily, model deployment faces challenges due to the specific characteristics of the industry.
FastEstimator: A Deep Learning Library for Fast Prototyping and Productization
As the complexity of state-of-the-art deep learning models increases by the month, implementation, interpretation, and traceability become ever-more-burdensome challenges for AI practitioners around the world.
