Google Inc.

Tushar Deepak Chandra

Tal Shaked

Yoram Singer

Tze Way Eugene Ie

Joshua A Redstone

Jeremiah Harmsen

Marcus Felipe Fontoura

Kenneth Jerome Goldman

Yonggang Zhao

James McFadden

Heng-Tze Cheng

Andrew Stephen Tomkins

Hrishikesh Balkrishna Aradhye

Gregory Sean Corrado

Wei Chai

Glen Anderson

Saurabh Mathur

Mustafa Ispir

Sarvjeet Singh

Alexandre Tachard Passos

Tomas Lloret Llinares

Jeremiah Joseph Harmsen

Mehmet Levent Koc

Rohan Anil

Xiaobing Liu

David Edgar Lluncor

Jerry Zhao

Shahar Jamshy

Peter Brandt

Kristen Riedt LeFevre

Jim McFadden

Danny Wyatt

Gustavo Menezes Ponte Moreira

Tze Way Eugene Le

Grady Simon

Systems and techniques are disclosed for generating entries for a searchable index based on rules generated by one or more machine-learned models. The index entries can include one or more tokens correlated with an outcome and an outcome probability. A subset of tokens can be identified based on the characteristics of an event. The index may be searched for outcomes and their respective probabilities that correspond to tokens that are similar to or match the subset of tokens based on the event.

Searchable index

Techniques are disclosed that enable the generation of a content agent based on content parameter(s) determined from an initial user request for content as well as a dialog session to further refine the request for content. Various implementations include using the content agent to render additional content responsive to an additional user request. Additional or alternatively implementations include using the content agent to proactively render content to the user.

Rendering content using a content agent and/or stored content parameter(s)

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training machine learning systems. One of the methods includes receiving a plurality of training examples; and training a machine learning system on each of the plurality of training examples to determine trained values for weights of a machine learning model, wherein training the machine learning system comprises: assigning an initial value for a regularization penalty for a particular weight for a particular feature; and adjusting the initial value for the regularization penalty for the particular weight for the particular feature during the training of the machine learning system.

Regularization relaxation scheme

A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.

Wide and deep machine learning models

Systems and techniques are provided for template exploration in a large-scale machine learning system. A method may include obtaining multiple base templates, each base template comprising multiple features. A template performance score may be obtained for each base template and a first base template may be selected from among the multiple base templates based on the template performance score of the first base template. Multiple cross-templates may be constructed by generating a cross-template of the selected first base template and each of the multiple base templates. Performance of a machine learning model may be tested based on each cross-template to generate a cross-template performance score for each of the cross-templates. A first cross-template may be selected from among the multiple cross-templates based on the cross-template performance score of the cross-template. Accordingly, the first cross-template may be added to the machine learning model.

Using template exploration for large-scale machine learning

The present disclosure provides systems and techniques for efficient locking of datasets in a database when updates to a dataset may be delayed. A method may include accumulating a plurality of updates to a first set of one or more values associated with one or more features. The first set of one or more values may be stored within a first database column. Next, it may be determined that a first database column update aggregation rule is satisfied. A lock assigned to at least a portion of at least a first database column may be acquired. Accordingly, one or more values in the first set within the first database column may be updated based on the plurality of updates. In an implementation, the first set of one or more values may be associated with the first lock.

Efficient locking of large data collections

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a factorization model to learning features of model inputs of a trained model such that the factorization model is predictive of outcome for which the machine learned model is trained.

Factorized models

The present disclosure provides methods and systems for using variable length representations of machine learning statistics. A method may include storing an n-bit representation of a first statistic at a first n-bit storage cell. A first update to the first statistic may be received, and it may be determined that the first update causes a first loss of precision of the first statistic as stored in the first n-bit storage cell. Accordingly, an m-bit representation of the first statistic may be stored at a first m-bit storage cell based on the determination. The first m-bit storage cell may be associated with the first n-bit storage cell. As a result, upon receiving an instruction to use the first statistic in a calculation, a combination of the n-bit representation and the m-bit representation may be used to perform the calculation.

Using variable length representations for machine learning statistics

Methods and systems for replacing feature values of features in training data with integer values selected based on a ranking of the feature values. The methods and systems are suitable for preprocessing large-scale machine learning training data.

Using an integerized representation for large-scale machine learning data

Parallel processing of data may include a set of map processes and a set of reduce processes. Each map process may include at least one map thread. Map threads may access distinct input data blocks assigned to the map process, and may apply an application specific map operation to the input data blocks to produce key-value pairs. Each map process may include a multiblock combiner configured to apply a combining operation to values associated with common keys in the key-value pairs to produce combined values, and to output intermediate data including pairs of keys and combined values. Each reduce process may be configured to access the intermediate data output by the multiblock combiners. For each key, an application specific reduce operation may be applied to the combined values associated with the key to produce output data.

Parallel processing of data

Growing community of inventors

Los Altos, CA, United States of America

Tushar Deepak Chandra