Computer Science Speaking Skills Talk

Speaking Skills
Ph.D. Student
Computer Science Department
Carnegie Mellon University
Nonparametric Neural Networks
Friday, June 23, 2017 - 2:30pm to 3:30pm
Gates Hillman Centers

Neural networks are functions used to convert raw data into useful information. They are represented by models with many parameters. Those parameters are generally optimized via gradient-based search. However, gradient-based search is indeed limited to tuning parameters of a given model. Choosing the model itself is an open problem. Models are generally chosen by expert judgement, for computational convenience or by brute force search.

I present "nonparametric neural networks", a framework for jointly optimizing both parameters and model. Under this framework, we alternate between local changes to the model and gradient-based parameter tuning. The search is enabled by defining a connected, continuous space over model-parameter pairs, by penalizing models according to their complexity, and by several optimization tricks.

Presented in Partial Fulfillment of the CSD Speaking Skills Requirement.

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