Patents by Inventor Haim Avron
Haim Avron has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11979309Abstract: A method includes computing a diffusion vector starting with a seed, querying nodes for connections, reweighting diffusion vector based on the degrees, sorting nodes based upon magnitude in the reweighted diffusion vector which is obtained through wave relaxation solution of a time-dependent initial value problem, detecting a community through a sweep over the nodes according to their rank, and selecting a prefix that minimizes or maximizes an objective function.Type: GrantFiled: November 30, 2015Date of Patent: May 7, 2024Assignee: International Business Machines CorporationInventors: Haim Avron, Lior Horesh, Raya Horesh, Omer Tripp
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Publication number: 20240135185Abstract: A method to determine data uncertainty is provided. The method receives a high dimensional data input and a corresponding data output. The method trains a variational autoencoder (VAE) with the high dimensional data input to learn a low dimensional latent space representation of the high dimensional data input. An encoder part of the VAE outputs a set of distributions of the high dimensional dataset in a latent space. The method samples new data samples in the latent space using the set of distributions outputs from the encoder part of the VAE. The method learns a polynomial chaos expansion to map the new data samples in the latent space to the corresponding data output to learn the set of distributions and their relation to perform estimation with high-dimensional dataset under uncertainty such as missing values by estimating the values using the set of distributions.Type: ApplicationFiled: February 10, 2023Publication date: April 25, 2024Inventors: Shashanka Ubaru, Paz Fink Shustin, Lior Horesh, Vasileios Kalantzis, Haim Avron
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Patent number: 11948093Abstract: Techniques for generating and managing, including simulating and training, deep tensor neural networks are presented. A deep tensor neural network comprises a graph of nodes connected via weighted edges. A network management component (NMC) extracts features from tensor-formatted input data based on tensor-formatted parameters. NMC evolves tensor-formatted input data based on a defined tensor-tensor layer evolution rule, the network generating output data based on evolution of the tensor-formatted input data. The network is activated by non-linear activation functions, wherein the weighted edges and non-linear activation functions operate, based on tensor-tensor functions, to evolve tensor-formatted input data. NMC trains the network based on tensor-formatted training data, comparing output training data output from the network to simulated output data, based on a defined loss function, to determine an update.Type: GrantFiled: October 5, 2022Date of Patent: April 2, 2024Assignees: INTERNATIONAL BUSINESS MACHINES CORPORATION, TRUSTEES OF TUFTS COLLEGE, RAMOT AT TEL-AVIV UNIVERSITY LTD.Inventors: Lior Horesh, Elizabeth Newman, Misha E. Kilmer, Haim Avron
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Patent number: 11790033Abstract: A computer implemented method for speeding up execution of a convex optimization operation one or more quadratic complexity operations to be performed by an analog crossbar hardware switch, and identifying one or more linear complexity operations to be performed by a CPU. At least one of the quadratic complexity operations is performed by the analog crossbar hardware, and at least one of the linear complexity operations is performed by the CPU. An iteration of an approximation of a solution to the convex optimization operation is updated by the CPU.Type: GrantFiled: September 16, 2020Date of Patent: October 17, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Vasileios Kalantzis, Shashanka Ubaru, Lior Horesh, Haim Avron, Oguzhan Murat Onen
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Publication number: 20230306276Abstract: Techniques for generating and managing, including simulating and training, deep tensor neural networks are presented. A deep tensor neural network comprises a graph of nodes connected via weighted edges. A network management component (NMC) extracts features from tensor-formatted input data based on tensor-formatted parameters. NMC evolves tensor-formatted input data based on a defined tensor-tensor layer evolution rule, the network generating output data based on evolution of the tensor-formatted input data. The network is activated by non-linear activation functions, wherein the weighted edges and non-linear activation functions operate, based on tensor-tensor functions, to evolve tensor-formatted input data. NMC trains the network based on tensor-formatted training data, comparing output training data output from the network to simulated output data, based on a defined loss function, to determine an update.Type: ApplicationFiled: October 5, 2022Publication date: September 28, 2023Inventors: Lior Horesh, Elizabeth Newman, Misha E. Kilmer, Haim Avron
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Patent number: 11544061Abstract: Methods and systems for solving a linear system include setting resistances in an array of settable electrical resistances in accordance with values of an input matrix. A series of input vectors is applied to the array as voltages to generate a series of respective output vectors. Each input vector in the series of vectors is updated based on comparison of the respective output vectors to a target vector. A solution of a linear system is determined that includes the input matrix based on the updated input vectors.Type: GrantFiled: December 22, 2020Date of Patent: January 3, 2023Assignees: INTERNATIONAL BUSINESS MACHINES CORPORATION, RAMOT AT TEL AVIV UNIVERSITY LTD.Inventors: Malte Johannes Rasch, Oguzhan Murat Onen, Tayfun Gokmen, Chai Wah Wu, Mark S. Squillante, Tomasz J. Nowicki, Wilfried Haensch, Lior Horesh, Vasileios Kalantzis, Haim Avron
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Patent number: 11531902Abstract: Techniques for generating and managing, including simulating and training, deep tensor neural networks are presented. A deep tensor neural network comprises a graph of nodes connected via weighted edges. A network management component (NMC) extracts features from tensor-formatted input data based on tensor-formatted parameters. NMC evolves tensor-formatted input data based on a defined tensor-tensor layer evolution rule, the network generating output data based on evolution of the tensor-formatted input data. The network is activated by non-linear activation functions, wherein the weighted edges and non-linear activation functions operate, based on tensor-tensor functions, to evolve tensor-formatted input data. NMC trains the network based on tensor-formatted training data, comparing output training data output from the network to simulated output data, based on a defined loss function, to determine an update.Type: GrantFiled: November 13, 2018Date of Patent: December 20, 2022Assignees: INTERNATIONAL BUSINESS MACHINES CORPORATION, TRUSTEES OF TUFTS COLLEGE, RAMOT AT TEL-AVIV UNIVERSITY LTD.Inventors: Lior Horesh, Elizabeth Newman, Misha E. Kilmer, Haim Avron
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Patent number: 11520855Abstract: A computer-implemented method is presented for performing matrix sketching by employing an analog crossbar architecture. The method includes low rank updating a first matrix for a first period of time, copying the first matrix into a dynamic correction computing device, switching to a second matrix to low rank update the second matrix for a second period of time, as the second matrix is low rank updated, feeding the first matrix with first stochastic pulses to reset the first matrix back to a first matrix symmetry point, copying the second matrix into the dynamic correction computing device, switching back to the first matrix to low rank update the first matrix for a third period of time, and as the first matrix is low rank updated, feeding the second matrix with second stochastic pulses to reset the second matrix back to a second matrix symmetry point.Type: GrantFiled: May 15, 2020Date of Patent: December 6, 2022Assignees: INTERNATIONAL BUSINESS MACHINES CORPORTATION, RAMOT AT TEL-AVIV UNIVERSITY, LTD.Inventors: Lior Horesh, Oguzhan Murat Onen, Haim Avron, Tayfun Gokmen, Vasileios Kalantzis, Shashanka Ubaru
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Patent number: 11500963Abstract: A method of performing Principal Component Analysis is provided. The method includes receiving, by a computing device, evolving data for processing/visualization. The method further includes, by the computing device, a dimensionality for reducing of the evolving data using the PCA, wherein the PCA is performed on analog crossbar hardware. The method also includes using, by the computing device, the evolving data for visualization having the dimensionality thereof reduced by the principal component analysis for a further application.Type: GrantFiled: October 1, 2020Date of Patent: November 15, 2022Assignees: INTERNATIONAL BUSINESS MACHINES CORPORATION, RAMOT AT TEL AVIV UNIVERSITY LTD.Inventors: Shashanka Ubaru, Vasileios Kalantzis, Lior Horesh, Mark S. Squillante, Haim Avron
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Publication number: 20220300575Abstract: Techniques for determining a count of triangles (tr) in a graph data structure using a crosspoint array is described. An adjacency matrix (a) representing the graph is mapped to the crosspoint array by configuring resistance values of crosspoint devices in the array. The count of triangles is initialized to zero (tr=0), and iteratively updated. The updating includes generating a first vector (x1) stochastically to include digital values in a predetermined range, which are converted into the voltage values. A multiplication of the adjacency matrix and the first vector (ax1) is computed using the crosspoint array. A second voltage vector (z1=ax1) is generated that includes voltage values representing the multiplication result. The adjacency matrix and the second voltage vector (z2=az1) are multiplied using the crosspoint array. The computer updates the number of triangles in the graph data structure as tr=tr+Z1T.Type: ApplicationFiled: March 22, 2021Publication date: September 22, 2022Inventors: Vasileios Kalantzis, Shashanka Ubaru, Haim Avron, Lior Horesh
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Patent number: 11386507Abstract: A computer-implemented method for analyzing a time-varying graph is provided. The time-varying graph includes nodes representing elements in a network, edges representing transactions between elements, and data associated with the nodes and the edges. The computer-implemented method includes constructing, using a processor, adjacency and feature matrices describing each node and edge of each time-varying graph for stacking into an adjacency tensor and describing the data of each time-varying graph for stacking into a feature tensor, respectively. The adjacency and feature tensors are partitioned into adjacency and feature training tensors and into adjacency and feature validation tensors, respectively. An embedding model and a prediction model are created using the adjacency and feature training tensors. The embedding and prediction models are validated using the adjacency and feature validation tensors to identify an optimized embedding-prediction model pair.Type: GrantFiled: September 23, 2019Date of Patent: July 12, 2022Assignees: INTERNATIONAL BUSINESS MACHINES CORPORATION, Trustees of Tufts College, RAMOT AT TEL-AVIV UNIVERSITY LTD.Inventors: Lior Horesh, Osman Asif Malik, Shashanka Ubaru, Misha E. Kilmer, Haim Avron
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Publication number: 20220197639Abstract: Methods and systems for solving a linear system include setting resistances in an array of settable electrical resistances in accordance with values of an input matrix. A series of input vectors is applied to the array as voltages to generate a series of respective output vectors. Each input vector in the series of vectors is updated based on comparison of the respective output vectors to a target vector. A solution of a linear system is determined that includes the input matrix based on the updated input vectors.Type: ApplicationFiled: December 22, 2020Publication date: June 23, 2022Inventors: Malte Johannes Rasch, Oguzhan Murat Onen, Tayfun Gokmen, Chai Wah Wu, Mark S. Squillante, Tomasz J. Nowicki, Wilfried Haensch, Lior Horesh, Vasileios Kalantzis, Haim Avron
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Publication number: 20220107991Abstract: A method of performing Principal Component Analysis is provided. The method includes receiving, by a computing device, evolving data for processing/visualization. The method further includes, by the computing device, a dimensionality for reducing of the evolving data using the PCA, wherein the PCA is performed on analog crossbar hardware. The method also includes using, by the computing device, the evolving data for visualization having the dimensionality thereof reduced by the principal component analysis for a further application.Type: ApplicationFiled: October 1, 2020Publication date: April 7, 2022Inventors: Shashanka Ubaru, Vasileios Kalantzis, Lior Horesh, Mark S. Squillante, Haim Avron
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Publication number: 20220083623Abstract: A computer implemented method for speeding up execution of a convex optimization operation one or more quadratic complexity operations to be performed by an analog crossbar hardware switch, and identifying one or more linear complexity operations to be performed by a CPU. At least one of the quadratic complexity operations is performed by the analog crossbar hardware, and at least one of the linear complexity operations is performed by the CPU. An iteration of an approximation of a solution to the convex optimization operation is updated by the CPU.Type: ApplicationFiled: September 16, 2020Publication date: March 17, 2022Inventors: Vasileios Kalantzis, Shashanka Ubaru, Lior Horesh, Haim Avron, Oguzhan Murat Onen
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Patent number: 11188616Abstract: An illustrative embodiment includes a method for solving a dynamical system. The method comprises: obtaining multidimensional snapshots representing respective discrete solutions of the dynamical system; storing the multidimensional snapshots within a snapshot tensor having an order of at least three; generating a basis for at least a subspace of a state space of the dynamical system at least in part by performing a decomposition of the snapshot tensor; deriving a reduced order model at least in part by using the basis to project the dynamical system from the state space onto the subspace; and solving the reduced order model of the dynamical system.Type: GrantFiled: February 25, 2020Date of Patent: November 30, 2021Assignees: International Business Machines Corporation, Trustees of Tufts College, Ramot at Tel Aviv University Ltd.Inventors: Lior Horesh, Misha Elena Kilmer, Haim Avron, Jiani Zhang
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Publication number: 20210357540Abstract: A computer-implemented method is presented for performing matrix sketching by employing an analog crossbar architecture. The method includes low rank updating a first matrix for a first period of time, copying the first matrix into a dynamic correction computing device, switching to a second matrix to low rank update the second matrix for a second period of time, as the second matrix is low rank updated, feeding the first matrix with first stochastic pulses to reset the first matrix back to a first matrix symmetry point, copying the second matrix into the dynamic correction computing device, switching back to the first matrix to low rank update the first matrix for a third period of time, and as the first matrix is low rank updated, feeding the second matrix with second stochastic pulses to reset the second matrix back to a second matrix symmetry point.Type: ApplicationFiled: May 15, 2020Publication date: November 18, 2021Inventors: Lior Horesh, Oguzhan Murat Onen, Haim Avron, Tayfun Gokmen, Vasileios Kalantzis, Shashanka Ubaru
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Publication number: 20210271730Abstract: An illustrative embodiment includes a method for solving a dynamical system. The method comprises: obtaining multidimensional snapshots representing respective discrete solutions of the dynamical system; storing the multidimensional snapshots within a snapshot tensor having an order of at least three; generating a basis for at least a subspace of a state space of the dynamical system at least in part by performing a decomposition of the snapshot tensor; deriving a reduced order model at least in part by using the basis to project the dynamical system from the state space onto the subspace; and solving the reduced order model of the dynamical system.Type: ApplicationFiled: February 25, 2020Publication date: September 2, 2021Inventors: Lior Horesh, Misha Elena Kilmer, Haim Avron, Jiani Zhang
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Patent number: 11017316Abstract: A computer-implemented method is presented for optimal experimental design to correct a misspecified model approximating a behavior of a dynamic system. The method includes formulating an experiment, determining experimental settings for configuring controllable experimental parameters based on mutual information and submodularity, measuring informative values associated with each choice of experimental design, as prescribed by the controllable experimental parameters, and learning a correction function based on the measured informative values. The computer-implemented method further includes determining an experimental design setup for gaining information content, and combining the experimental design setup with the experimental settings to construct a corrected model of the dynamic system.Type: GrantFiled: June 6, 2017Date of Patent: May 25, 2021Assignees: International Business Machines Corporation, RAMOT AT TEL-AVIV UNIVERSITY LTD.Inventors: Haim Avron, Guy M. Cohen, Lior Horesh, Raya Horesh, Gal Shulkind
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Publication number: 20210090182Abstract: A computer-implemented method for analyzing a time-varying graph is provided. The time-varying graph includes nodes representing elements in a network, edges representing transactions between elements, and data associated with the nodes and the edges. The computer-implemented method includes constructing, using a processor, adjacency and feature matrices describing each node and edge of each time-varying graph for stacking into an adjacency tensor and describing the data of each time-varying graph for stacking into a feature tensor, respectively. The adjacency and feature tensors are partitioned into adjacency and feature training tensors and into adjacency and feature validation tensors, respectively. An embedding model and a prediction model are created using the adjacency and feature training tensors. The embedding and prediction models are validated using the adjacency and feature validation tensors to identify an optimized embedding-prediction model pair.Type: ApplicationFiled: September 23, 2019Publication date: March 25, 2021Inventors: Lior Horesh, Osman Asif Malik, Shashanka Ubaru, Misha E. Kilmer, Haim Avron
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Patent number: 10771088Abstract: A tensor decomposition method, system, and computer program product include compressing multi-dimensional data by truncated tensor-tensor decompositions.Type: GrantFiled: February 28, 2019Date of Patent: September 8, 2020Assignees: INTERNATIONAL BUSINESS MACHINES CORPORATION, TUFTS UNIVERSITY, TEL AVIV-YAFO UNIVERSITYInventors: Lior Horesh, Misha E Kilmer, Haim Avron, Elizabeth Newman