FURI | Spring 2019


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Alpha-loss is a tunable loss function with a parameter alpha that bridges log-loss for (alpha = 1) and 0-1 loss for (alpha = infinity). In machine learning literature, the theoretically optimal loss function is the 0-1 loss function. Unfortunately, 0-1 loss is computationally intractable due to being non-differentiable and discontinuous. Thus, it will be extremely beneficial to continue exploring such a surrogate loss function and how well it can emulate 0-1 loss. Discovering such desirable properties of this loss function will provide engineers and machine learning enthusiast better performance and more variability in neural network designs.

Student researcher

Portrait of Ott, Corbin

Corbin Ott

Electrical engineering

Hometown: Indianapolis, Indiana

Graduation date: Fall 2018