Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for privacy-sensitive training of a neural network. In one aspect, a system comprises a central memory configured to store current values of a set of neural network parameters and one or more computers that are configured to implement a plurality of worker computing units, where each worker computing unit is configured to repeatedly perform operations comprising obtaining current values of the set of neural network parameters from the central memory, sampling a batch of network inputs from a set of training data, determining a respective gradient corresponding to each network input, determining an aggregated gradient based on the gradients, identifying a subset of a set of gradient values as target values, generating a noisy gradient by combining random noise with the target gradient values, and updating the current values of the set of neural network parameters.

Abstract: Computer-implemented systems and methods for training a decentralized model for making a personalized recommendation.

Abstract: A machine learning classifier is used to determine whether a web page belongs to a blog, based on a number of characteristics of web pages (e.g., presence of words such as “permalink”, or being hosted on a known blogging site). The classifier may be initially trained using human-judged examples. After classifying web pages as being blog pages, the blog pages may be further identified or categorized as top level blogs based on their URLs, for example.

Abstract: A machine learning classifier is used to determine whether a web page belongs to a blog, based on a number of characteristics of web pages (e.g., presence of words such as “permalink”, or being hosted on a known blogging site). The classifier may be initially trained using human-judged examples. After classifying web pages as being blog pages, the blog pages may be further identified or categorized as top level blogs based on their URLs, for example.