4 citations · 11 across the 5 of their papers we have counts for
5 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…
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…
Impact of Inference Accelerators on hardware selection
Dibyajyoti Pati, Caroline Favart, Purujit Bahl +6
As opportunities for AI-assisted healthcare grow steadily, model deployment faces challenges due to the specific characteristics of the industry. The configuration choice for a pro…
AI Assisted Annotator using Reinforcement Learning
V. Ratna Saripalli, Gopal Avinash, Dibyajyoti Pati +2
Healthcare data suffers from both noise and lack of ground truth. The cost of data increases as it is cleaned and annotated in healthcare. Unlike other data sets, medical data anno…