AI News, Categories artificial intelligence

Amazon researchers’ method adds classes to AI classifiers more quickly

In a new blog post and accompanying paper (“Transfer Learning for Sequence Labeling using Source Model and Target Data”), researchers at Amazon’s Alexa division describe a method for updating a classifier using only training data for the new class.

This, they say, demonstrates that it’s possible to transfer an AI system and its learned parameters —  the values used to control certain properties of the model —

“Alexa scientists and engineers have poured a great deal of effort into Alexa’s core functionality, but through the Alexa Skills Kit, we’ve also enabled third-party developers to build their own Alexa skills —

that we study in the new paper would make it possible for third-party developers to make direct use of our in-house systems without requiring access to in-house training data.”

Additionally, they tried two different transfer learning methods: one that relied on the aforementioned neural adapter, and another than expanded the size of the trained classifer’s output layer of functions and the layers of functions immediately beneath it.

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