Google Inc.

Twitter, Inc.

Michael Jahr

Uri N Lerner

Ashok Chhabedia Popat

Sandeep Pandey

Henry Allan Rowley

Zhifei Li

Utkarsh Srivastava

Franz Josef Och

Ashish Goel

Parag Agrawal

Reid Andersen

Jeremy Ginsberg

Yue Lu

James Neufeld

Cheng Li

Ignacio E Thayer

Vishal Kasera

Dmitriy Genzel

A real-time messaging platform allows advertiser accounts to pay to insert candidate messages into the message streams requested by account holders. To accommodate multiple advertisers, the messaging platform controls an auction process that determines which candidate messages are selected for inclusion in a requested account holder's message stream. Selection is based on a bid for the candidate message, the message stream that is requested, and a variety of other factors that vary depending upon the implementation. The process for selection of candidate messages generally includes the following steps, though any given step may be omitted or combined into another step in a different implementation: targeting, filtering, prediction, ranking, and selection.

Exploration in a real time messaging platform

In some examples, a computing device includes at least one processor and at least one module, operable by the at least one processor to receive, from a client device of a user, a request for one or more advertisements to display at the client device with a set of messages. The set of messages is associated with the user in a social network messaging service. The at least one module may be further operable to determine a probability that the user will select a candidate advertisement using a machine learning model based on point-wise learning and pair-wise learning. The at least one module may be further operable to determine, based on the probability that the user will select the candidate advertisement, a candidate score for the candidate advertisement, determine that the candidate score satisfies a threshold, and send, for display at the client device, the candidate advertisement.

Click-through prediction for targeted content

Capturing information from an imprinted item comprises a computing device that receives an image of a back side of an imprinted item, the payment instrument comprising information imprinted thereon such that the imprinted information protrudes from a front side of the imprinted item and the imprinted information is indented into the back side of the imprinted item. The computing device extracts sets of characters from the image of the back side of the imprinted item based on the imprinted information indented into the back side of the imprinted item and depicted in the image of the back side of the imprinted item. The computing device applies a first character recognition application to process the sets of characters extracted from the image of the back side of the imprinted item and categorizes each of the sets of characters into one of a plurality of categories relating to the information.

Extraction of data from a digital image

One embodiment of the present invention provides a system that merges similar clusters of conceptually-related words in a probabilistic generative model for textual documents. During operation, the system receives a current model, which contains terminal nodes representing random variables for words and contains cluster nodes representing clusters of conceptually related words. Nodes in the current model are coupled together by weighted links, wherein if a node fires, a link from the node to another node causes the other node to fire with a probability proportionate to the weight of the link. Next, the system determines whether cluster nodes in the current model explain other cluster nodes in the current model. If two cluster nodes explain each other, the system merges the two cluster nodes to form a combined cluster node.

Selectively merging clusters of conceptually related words in a generative model for text

A method may include receiving, at one or more processors, a current model. The current model may include a group of nodes representing words, at least one cluster of nodes representing related words, and a group of links. Each link may connect two nodes of the group of nodes. Each link may include a corresponding weight. The method may further include applying, by one or more processors, a set of training documents to the model to produce new weights for the group of links to create a new model; and making, by one or more processors, the new model the current model.

Method and apparatus for selecting links to include in a probabilistic generative model for text

Capturing information from payment instruments comprises receiving, using one or more computer devices, an image of a back side of a payment instrument, the payment instrument comprising information imprinted thereon such that the imprinted information protrudes from a front side of the payment instrument and the imprinted information is indented into the back side of the payment instrument; extracting sets of characters from the image of the back side of the payment instrument based on the imprinted information indented into the back side of the payment instrument and depicted in the image of the back side of the payment instrument; applying a first character recognition application to process the sets of characters extracted from the image of the back side of the payment instrument; and categorizing each of the sets of characters into one of a plurality of categories relating to information required to conduct a payment transaction.

Extraction of financial account information from a digital image of a card

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing optical character recognition. In one aspect, a method includes receiving a text image I. A set of feature functions are evaluated for a log linear model to determine respective feature values for the text image I, wherein each feature function hmaps the text image I to a feature value, and wherein each feature function his associated with a respective feature weight &#x3bb;. A transcription {circumflex over (T)} is determined that minimizes a cost of the log linear model.

Optical character recognition

Systems, methods, and apparatuses including computer program products for machine learning. A method is provided that includes determining model parameters for a plurality of feature functions for a linear machine learning model, ranking the plurality of feature functions according to a quality criterion, and selecting, using the ranking, a group of feature functions from the plurality of feature functions to update with the determined model parameters.

Minimum error rate training with a large number of features for machine learning

Some embodiments of the present invention provide a system that selects links while updating a probabilistic generative model for textual documents. During operation, the system receives a current model, which contains terminal nodes representing words and cluster nodes representing clusters of conceptually related words, wherein nodes in the current model are coupled together by weighted links, wherein if a node fires, a link from the node to another node is activated and causes the other node to fire with a probability proportionate to the weight of the link. Next, the system applies a set of training documents containing words to the current model to produce a new model. While doing so, the system: determines expected counts for activations of links and prospective links; determines link-ratings for the links and the prospective links based on the expected counts, and selects links to be included in the new model based on the determined link-ratings. Finally, the system makes the new model the current model.

One embodiment of the present invention provides a system that selectively deletes clusters of conceptually-related words from a probabilistic generative model for textual documents. During operation, the system receives a current model, which contains terminal nodes representing random variables for words and contains one or more cluster nodes representing clusters of conceptually related words. Nodes in the current model are coupled together by weighted links, so that if an incoming link from a node that has fired causes a cluster node to fire with a probability proportionate to a weight of the incoming link, an outgoing link from the cluster node to another node causes the other node to fire with a probability proportionate to the weight of the outgoing link. Next, the system processes a given cluster node in the current model for possible deletion. This involves determining a number of outgoing links from the given cluster node to terminal nodes or cluster nodes in the current model. If the determined number of outgoing links is less than a minimum value, or if the frequency with which the given cluster node fires is less than a minimum frequency, the system deletes the given cluster node from the current model.

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San Francisco, CA, United States of America

Michael Jahr