Harnessing Neural Networks

Harnessing Neural Networks: Deep learning has demonstrated impressive performance gain in many machine learning applications. However, unveiling and realizing these performance gains is not always straightforward. Discovering the right network architecture is critical for accuracy and often requires a human in the loop. Some network architectures occasionally produce spurious outputs, and the outputs have to be restricted to meet the needs of an application. Finally, realizing the performance gain in a production system can be difficult because of extensive inference times. In this talk we discuss methods for making neural networks efficient in production systems. We also discuss an efficient method for automatically learning the network architecture, called AdaNet. We provide theoretical arguments for the algorithm and present experimental evidence for its effectiveness.

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