Microsoft Technology Licensing, LLC

Manik Varma

Ashish Kumar

Debajyoti Ray

Deepak Saini

Sachin Yadav

Anirudh Buvanesh

Bhawna Paliwal

A computing system obtains a novel keyword from a keyword database comprising a plurality of keywords. The computing system provides the novel keyword as input into an encoder and obtains a textual embedding of the novel keyword. The computing system identifies classifiers for similar seen keywords to the novel keyword. The novel keyword embedding and the classifiers are provided as input into a combiner model which outputs a textual embedding representative of the novel keyword and the seen keyword classifiers. The computing system compares the output of the combiner model to a textual embedding of query text. The computing system then identifies content based upon a computed similarity between the query text and the output of the combiner model. The computing system causes presentation of search results at a client computing device wherein the results include content selected based upon the computed similarity.

Retrieval of novel keywords for search

Generally discussed herein are devices, systems, and methods for machine-learning. A method may include projecting an input feature vector of a first dimensional space into a second dimensional space to create a lower dimensional feature vector, the second dimensional space smaller than the first dimensional space, determining a first prediction vector for an internal node of the tree, determining whether to pass the first prediction vector to a first child or a second child of the internal node based on a sparse vector and the lower dimensional feature vector, determining a second prediction vector at a leaf node of the tree, and determining an overall prediction by combining the first and second prediction vectors.

Resource-efficient machine learning

Systems and methods are described for learning the discriminative power-invariance tradeoffs for classification of input data ('tradeoff learning system'). In various embodiments, the tradeoff learning system receives multiple classifiers ('base classifiers') and employs a learning technique to produce a combined classifier. Each received base classifier achieves a different level of tradeoff. The learning technique then decreases a function of kernel weights associated with each of the received classifiers to produce the combined classifier. By decreasing the function of kernel weights, the tradeoff learning system computes a combined classifier that classifies input data more accurately than the received multiple classifiers.

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Manik Varma