Abstract: Example aspects of the present disclosure provide a novel, resource-efficient approach for learning image representation with federated learning, which can be referred to as federated sampled SoftMax. According to example aspects of the present disclosure, the federated learning clients sample a set of negative classes and optimize only the corresponding model parameters with respect to a sampled SoftMax objective that approximates the global full SoftMax objective. This approach significantly reduces the number of parameters transferred to and optimized by the client devices, while performing on par with the standard full SoftMax method. This creates a possibility for efficiently learning image representations on decentralized data with a large number of classes in a privacy preserving way.

Abstract: An embodiment of the present invention generates natural language content from a set of keywords in accordance with a template. Keyword vectors representing a context for the keywords are generated. The keywords are associated with language tags, while the template includes a series of language tags indicating an arrangement for the generated natural language content. Template vectors are generated from the series of language tags of the template and represent a context for the template. Contributions from the contexts for the keywords and the template are determined based on a comparison of the series of language tags of the template with the associated language tags of the keywords. One or more words for each language tag of the template are generated to produce the natural language content based on combined contributions from the contexts for the keywords and the template.

Abstract: An embodiment of the present invention generates natural language content from a set of keywords in accordance with a template. Keyword vectors representing a context for the keywords are generated. The keywords are associated with language tags, while the template includes a series of language tags indicating an arrangement for the generated natural language content. Template vectors are generated from the series of language tags of the template and represent a context for the template. Contributions from the contexts for the keywords and the template are determined based on a comparison of the series of language tags of the template with the associated language tags of the keywords. One or more words for each language tag of the template are generated to produce the natural language content based on combined contributions from the contexts for the keywords and the template.