Hewlett-packard Development Company, L.P.

Hewlett Packard Enterprise Development LP

Hewlett-packard Company

Verizon Patent and Licensing Inc.

Yahoo Assets LLC

Other

Oath Inc.

Verizon Media Inc.

Yahoo Ad Tech LLC

At&t Laboratories, Limited

Erik Ordentlich

Gadiel Seroussi

Marcelo Weinberger

Itschak Weissman

Ron M Roth

Sergio Verdu

Naveen Muralimanohar

Andrew Feng

Ron Roth

Yoocharn Jeon

Raul Hernan Etkin

Giovanni Motta

Craig Michael Wittenbrink

Peter Cnudde

Pascal Vontobel

Lee Yang

Michael Renne Ty Tan

Gregg Bernard Lesartre

Jun Shi

Martin Foltin

Krishnamurthy Viswanathan

Faizaan Charania

Milind Rao

Tsachy Weissman

Ignacio Ramirez

Richard Stanley Williams

Gary A Gibson

Brent Buchanan

Richard H Henze

Amit S Sharma

Yair Shoham

Raul Herman Etkin

Cong Xu

James Ignowski

David S Taubman

Christos Chrysafis

Gadlel Seroussi

Ignaclo Ramirez

George Gemelos

Jacquelyn M Ingemi

David Scott Taubman

Raul Heman Etkin

Cheng Chang

Sergio Verdú

The present teaching relates to method, system, medium, and implementations for identifying k nearest neighbors. One or more KNN lists corresponding to one or more source data points are received. Each KNN list includes K neighbors of a source data point and each of the K neighbors is a data point represented by an index. Neighbor pairs and reverse neighbor pairs are generated based on the one or more KNN lists. The neighbor pairs and reverse neighbor pairs having the same source data point are grouped to generate a grouped pairs of neighbors for the source data point. A local join operation is performed based on grouped pairs of neighbors for each source data point to generate a combined neighborhood for the source data point, which is then sent to a KNN server, where combined neighborhoods generated by multiple local join executors are integrated to update a plurality of global KNN lists.

System and method for identifying approximate K-nearest neighbors in web scale clustering

The present teaching relates to obtaining a model for identifying content matching a query. Training data are received which include queries, advertisements, and hyperlinks. A plurality of subwords are identified from each of the queries and a plurality of vectors for the plurality of subwords of each of the queries are obtained. Via a neural network, a vector for each of the queries is derived based on a plurality of vectors for the plurality of subwords of the query. A query/ads model is obtained via optimization with respect to an objective function, based on vectors associated with the plurality of subwords of each of the queries and vectors for the queries obtained from the neural network.

System and method for query to ad matching using deep neural net based query embedding

The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Method and system for distributed machine learning

System and method for identifying approximate k-nearest neighbors in web scale clustering

The present teaching relates to obtaining a model for identifying content matching a query. Training data are received which include queries, advertisements, and hyperlinks. A plurality of subwords are identified from each of the queries and a plurality of input vectors for the plurality of subwords of each of the queries are obtained and an input vector for each of the queries is derived based on a plurality of input vectors of a plurality of subwords of the query. A query/ads model is optimized with respect to an objective function via training an input vector for each of the plurality of subwords associated with each of the queries, an input vector for each of the advertisements and links, and a matrix.

System and method for query to ad matching via optimizing input vectors of query subwords

Bit-flip coding uses a bit-flip encoder to flip bits in a redundancy-intersecting vector of a binary array having n rows and n columns until Hamming weights of the binary array are within a predetermined range &#x394; of n divided by two. Information bits of an input data word to the bit-flip coding apparatus are stored in locations within the binary array that are not occupied by n redundancy bits of a redundancy vector.

Bit-flip coding

In an example, in a method for encoding data within a crossbar memory array containing cells, bits of input data may be received. The received bits of data may be mapped to the cells in a row of the memory array, in which the cells are to be assigned to one of a low resistance state and a high resistance state. A subset of the mapped bits in the row may be grouped into a word pattern. The word pattern may be arranged such that more low resistance states are mapped to cells that are located closer to a voltage source of the row of the memory array than to cells that are located farther away from the voltage source.

Encoding data within a crossbar memory array

According to an example, a method for assigning redundancy in encoding data onto crossbar memory arrays is provided wherein each of said crossbar memory arrays include cells. The data may be allocated to a subset of the cells in multiple crossbar memory arrays. The redundancy for the data may then be assigned based on coordinates of the subset of cells within the multiple crossbar memory arrays onto which the data is allocated.

Assigning redundancy in encoding data onto crossbar memory arrays

In one implementation, a data storage system includes a memory array having memory devices in a crossbar configuration, and a memory controller for controlling data storage in the memory array. The memory controller includes an encoder to generate a 2-dimensional encoded bit pattern that encodes an input data. Each run-length of 0's and each run-length of 1's in each row or each column of the encoded bit pattern are at least of a predefined lower limit. The predefined lower limit is at least two. The memory controller includes a write controller to write the encoded bit pattern into the memory devices of the memory array, such that a number of consecutive memory devices in each row or each column of the memory array having a same state is based on the encoded bit pattern.

Storing run-length limited two-dimensional encoded bit patterns in memory arrays

A method to access two memory cells include determining a first cell current flowing through a first memory cell by subtracting a sneak current associated with the first memory cell from a first access current of the first bitline and determining a second cell current flowing through a second memory cell in the first bitline or a second bitline by subtracting the sneak current associated with the first memory cell from a second access current of the first bitline or the second bitline.

Reusing sneak current in accessing memory cells

Encoding or decoding can operate a processing system to apply one or more recursive relations to a known parameter associated with a length m and a Hamming weight l to produce a computed parameter associated with length m&#x2212;1. An encoding process can thus assign values to bits of a code based on comparison of the data value being encoded and the computed parameter. A decoding process can use the computed parameters in a calculation of a decoded data value.

Constant hamming weight coding

This disclosure provides a circuit that includes a ramp generator to apply a voltage ramp to a resistive memory cell. A sensing circuit can enable the ramp generator and monitor a current output received from the resistive memory cell in response to the applied voltage ramp, wherein the sensing circuit compares the current output to a predetermined current threshold to determine the state of the resistive memory cell.

Sensing circuit for resistive memory

Methods and systems for storing data in memory arrays are described. In one implementation, input bits are encoded into an intermediate binary array having multiple sub-arrays iteratively appended row-wise to the intermediate binary array. First sub-array is generated based on the input bits such that each row of the first sub-array has a number of 1's equal to a fraction of number of columns in the first sub-array, and based on a column balance coding such that the columns of the first sub-array have an equal number of 1's. At least one subsequent sub-array is generated based on a set of bits obtained from balancing termination indices for a previous sub-array and from diagonal bits of the intermediate binary array that are a part of the previous appended sub-array. The intermediate binary array is transformed to an encoded bit pattern. The encoded bit pattern is stored in the memory array.

Data storing in memory arrays

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San Jose, CA, United States of America

Erik Ordentlich