Abstract: This document relates to automated generation of machine learning models, such as neural networks. One example method involves obtaining a first machine learning model having one or more first inference operations. The example method also involves identifying a plurality of second inference operations that are supported by an inference hardware architecture. The example method also involves generating second machine learning models by modifying the first machine learning model to include individual second inference operations that are supported by the inference hardware architecture. The example method also involves selecting a final machine learning model from the second machine learning models based on one or more metrics.

Abstract: Methods, systems, and computer-readable media for search query extension are disclosed. A particular method retrieves a set of queries related to a search query from a query repository. The search query includes one or more search words and at least one search word is included in each query in the set of queries. A set of context words based on one or more computer-readable files accessible to a computer is generated and an intersection is calculated. The intersection is based on the set of queries and the set of context words. An extended search query is generated based on the calculated intersection and the search query.

Abstract: Methods, systems, and computer-readable media for search query extension are disclosed. A particular method retrieves a set of queries related to a search query from a query repository. The search query includes one or more search words and at least one search word is included in each query in the set of queries. A set of context words based on one or more computer-readable files accessible to a computer is generated and an intersection is calculated. The intersection is based on the set of queries and the set of context words. An extended search query is generated based on the calculated intersection and the search query.