3 citations · 7 across the 4 of their papers we have counts for
4 papers
To Raise or Not To Raise: The Autonomous Learning Rate Question
Xiaomeng Dong, Tao Tan, Michael Potter +4
There is a parameter ubiquitous throughout the deep learning world: learning rate. There is likewise a ubiquitous question: what should that learning rate be? The true answer to th…
Optimizing Data Augmentation Policy Through Random Unidimensional Search
Xiaomeng Dong, Michael Potter, Gaurav Kumar +3
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 a…
Solving Physics Puzzles by Reasoning about Paths
Augustin Harter, Andrew Melnik, Gaurav Kumar +3
We propose a new deep learning model for goal-driven tasks that require intuitive physical reasoning and intervention in the scene to achieve a desired end goal. Its modular struct…
FastEstimator: A Deep Learning Library for Fast Prototyping and Productization
Xiaomeng Dong, Junpyo Hong, Hsi-Ming Chang +10
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 pr…