3 citations · 8 across the 6 of their papers we have counts for
6 papers
Adversarial Focal Loss: Asking Your Discriminator for Hard Examples
Chen Liu, Xiaomeng Dong, Michael Potter +2
Focal Loss has reached incredible popularity as it uses a simple technique to identify and utilize hard examples to achieve better performance on classification. However, this meth…
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…
Parallel Matrix Condensation for Calculating Log-Determinant of Large Matrix
Xiaomeng Dong, EN Barnett, Sudarshan K. Dhall
Calculating the log-determinant of a matrix is useful for statistical computations used in machine learning, such as generative learning which uses the log-determinant of the covar…