Patents by Inventor RAANAN YONATAN YEHEZKEL ROHEKAR
RAANAN YONATAN YEHEZKEL ROHEKAR 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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Publication number: 20240005137Abstract: A mechanism is described for facilitating on-the-fly deep learning in machine learning for autonomous machines. A method of embodiments, as described herein, includes detecting an output associated with a first deep network serving as a user-independent model associated with learning of one or more neural networks at a computing device having a processor coupled to memory. The method may further include automatically generating training data for a second deep network serving as a user-dependent model, where the training data is generated based on the output. The method may further include merging the user-independent model with the user-dependent model into a single joint model.Type: ApplicationFiled: September 19, 2023Publication date: January 4, 2024Applicant: Intel CorporationInventor: RAANAN YONATAN YEHEZKEL ROHEKAR
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Publication number: 20230394305Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to receive a plurality of data inputs for training a neural network, wherein the data inputs comprise training data and weights inputs; represent the data inputs in a first form; and represent the weight inputs in a second form. Other embodiments are also disclosed and claimed.Type: ApplicationFiled: May 30, 2023Publication date: December 7, 2023Applicant: Intel CorporationInventors: Lev Faivishevsky, Tomer Bar-On, Yaniv Fais, Jacob Subag, Jeremie Dreyfuss, Amit Bleiweiss, Tomer Schwartz, Raanan Yonatan Yehezkel Rohekar, Michael Behar, Amitai Armon, Uzi Sarel
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Publication number: 20230376739Abstract: A mechanism is described for facilitating the transfer of features learned by a user-independent pre-trained deep neural network to a user-dependent neural network. The mechanism includes extracting a feature learned by a first deep neural network (DNN) model via the framework, wherein the first DNN model is a pre-trained DNN model for computer vision to enable user-independent classification of an object within an input video frame and training, via the deep learning framework, a second DNN model for computer vision based on the extracted feature, the second DNN model an update of the first DNN model, wherein training the second DNN model includes training the second DNN model based on a dataset including user-dependent data.Type: ApplicationFiled: May 23, 2023Publication date: November 23, 2023Applicant: Intel CorporationInventor: RAANAN YONATAN YEHEZKEL ROHEKAR
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Publication number: 20230325628Abstract: Causal explanations of outputs of a neural network can be learned from an attention layer in the neural network. The neural network may compute an output variable by processing a variable set including one or more input variables. An attention matrix may be computed by the attention layer in an abductive inference for which a new variable set including the input variables and the output variable is input into the neural network. Causal relationship between the variables in the new variable set may be determined based on the attention matrix and illustrated in a causal graph. A tree structure may be generated based on the causal graph. An input variable may be identified using the tree structure and determined to be the reason why the neural network computed the output variable. An explanation of the causal relation between the input variable and output variable can be generated and provided.Type: ApplicationFiled: May 30, 2023Publication date: October 12, 2023Inventors: Shami Nisimov, Raanan Yonatan Yehezkel Rohekar, Yaniv Gurwicz, Guy Koren, Gal Novik
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Patent number: 11704564Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to receive a plurality of data inputs for training a neural network, wherein the data inputs comprise training data and weights inputs; represent the data inputs in a first form; and represent the weight inputs in a second form. Other embodiments are also disclosed and claimed.Type: GrantFiled: August 17, 2021Date of Patent: July 18, 2023Assignee: INTEL CORPORATIONInventors: Lev Faivishevsky, Tomer Bar-On, Yaniv Fais, Jacob Subag, Jeremie Dreyfuss, Amit Bleiweiss, Tomer Schwartz, Raanan Yonatan Yehezkel Rohekar, Michael Behar, Amitai Armon, Uzi Sarel
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Patent number: 11698930Abstract: Various embodiments are generally directed to techniques for determining artificial neural network topologies, such as by utilizing probabilistic graphical models, for instance. Some embodiments are particularly related to determining neural network topologies by bootstrapping a graph, such as a probabilistic graphical model, into a multi-graphical model, or graphical model tree. Various embodiments may include logic to determine a collection of sample sets from a dataset. In various such embodiments, each sample set may be drawn randomly for the dataset with replacement between drawings. In some embodiments, logic may partition a graph into multiple subgraph sets based on each of the sample sets. In several embodiments, the multiple subgraph sets may be scored, such as with Bayesian statistics, and selected amongst as part of determining a topology for a neural network.Type: GrantFiled: June 21, 2018Date of Patent: July 11, 2023Assignee: INTEL CORPORATIONInventors: Yaniv Gurwicz, Raanan Yonatan Yehezkel Rohekar, Shami Nisimov, Guy Koren, Gal Novik
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Patent number: 11663456Abstract: A mechanism is described for facilitating the transfer of features learned by a context independent pre-trained deep neural network to a context dependent neural network. The mechanism includes extracting a feature learned by a first deep neural network (DNN) model via the framework, wherein the first DNN model is a pre-trained DNN model for computer vision to enable context-independent classification of an object within an input video frame and training, via the deep learning framework, a second DNN model for computer vision based on the extracted feature, the second DNN model an update of the first DNN model, wherein training the second DNN model includes training the second DNN model based on a dataset including context-dependent data.Type: GrantFiled: August 12, 2021Date of Patent: May 30, 2023Assignee: Intel CorporationInventor: Raanan Yonatan Yehezkel Rohekar
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Publication number: 20230117143Abstract: A mechanism is described for facilitating learning and application of neural network topologies in machine learning at autonomous machines. A method of embodiments, as described herein, includes monitoring and detecting structure learning of neural networks relating to machine learning operations at a computing device having a processor, and generating a recursive generative model based on one or more topologies of one or more of the neural networks. The method may further include converting the generative model into a discriminative model.Type: ApplicationFiled: November 8, 2022Publication date: April 20, 2023Applicant: Intel CorporationInventors: RAANAN YONATAN YEHEZKEL ROHEKAR, Guy Koren, Shami Nisimov, Gal Novik
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Patent number: 11501152Abstract: A mechanism is described for facilitating learning and application of neural network topologies in machine learning at autonomous machines. A method of embodiments, as described herein, includes monitoring and detecting structure learning of neural networks relating to machine learning operations at a computing device having a processor, and generating a recursive generative model based on one or more topologies of one or more of the neural networks. The method may further include converting the generative model into a discriminative model.Type: GrantFiled: July 26, 2017Date of Patent: November 15, 2022Assignee: INTEL CORPORATIONInventors: Raanan Yonatan Yehezkel Rohekar, Guy Koren, Shami Nisimov, Gal Novik
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Patent number: 11354542Abstract: A mechanism is described for facilitating on-the-fly deep learning in machine learning for autonomous machines. A method of embodiments, as described herein, includes detecting an output associated with a first deep network serving as a user-independent model associated with learning of one or more neural networks at a computing device having a processor coupled to memory. The method may further include automatically generating training data for a second deep network serving as a user-dependent model, where the training data is generated based on the output. The method may further include merging the user-independent model with the user-dependent model into a single joint model.Type: GrantFiled: February 6, 2020Date of Patent: June 7, 2022Assignee: Intel CorporationInventor: Raanan Yonatan Yehezkel Rohekar
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Publication number: 20220076118Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to receive a plurality of data inputs for training a neural network, wherein the data inputs comprise training data and weights inputs; represent the data inputs in a first form; and represent the weight inputs in a second form. Other embodiments are also disclosed and claimed.Type: ApplicationFiled: August 17, 2021Publication date: March 10, 2022Applicant: Intel CorporationInventors: Lev Faivishevsky, Tomer Bar-On, Yaniv Fais, Jacob Subag, Jeremie Dreyfuss, Amit Bleiweiss, Tomer Schwartz, Raanan Yonatan Yehezkel Rohekar, Michael Behar, Amitai Armon, Uzi Sarel
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Publication number: 20220067440Abstract: A mechanism is described for facilitating the transfer of features learned by a context independent pre-trained deep neural network to a context dependent neural network. The mechanism includes extracting a feature learned by a first deep neural network (DNN) model via the framework, wherein the first DNN model is a pre-trained DNN model for computer vision to enable context-independent classification of an object within an input video frame and training, via the deep learning framework, a second DNN model for computer vision based on the extracted feature, the second DNN model an update of the first DNN model, wherein training the second DNN model includes training the second DNN model based on a dataset including context-dependent data.Type: ApplicationFiled: August 12, 2021Publication date: March 3, 2022Applicant: Intel CorporationInventor: RAANAN YONATAN YEHEZKEL ROHEKAR
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Patent number: 11238338Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to receive a plurality of data inputs for training a neural network, wherein the data inputs comprise training data and weights inputs; represent the data inputs in a first form; and represent the weight inputs in a second form. Other embodiments are also disclosed and claimed.Type: GrantFiled: April 24, 2017Date of Patent: February 1, 2022Assignee: INTEL CORPORATIONInventors: Lev Faivishevsky, Tomer Bar-On, Yaniv Fais, Jacob Subag, Jeremie Dreyfuss, Amit Bleiweiss, Tomer Schwartz, Raanan Yonatan Yehezkel Rohekar, Michael Behar, Amital Armon, Uzi Sarel
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Patent number: 11120304Abstract: A mechanism is described for facilitating the transfer of features learned by a context independent pre-trained deep neural network to a context dependent neural network. The mechanism includes extracting a feature learned by a first deep neural network (DNN) model via the framework, wherein the first DNN model is a pre-trained DNN model for computer vision to enable context-independent classification of an object within an input video frame and training, via the deep learning framework, a second DNN model for computer vision based on the extracted feature, the second DNN model an update of the first DNN model, wherein training the second DNN model includes training the second DNN model based on a dataset including context-dependent data.Type: GrantFiled: July 15, 2020Date of Patent: September 14, 2021Assignee: Intel CorporationInventor: Raanan Yonatan Yehezkel Rohekar
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Patent number: 11010658Abstract: A recursive method and apparatus produce a deep convolution neural network (CNN). The method iteratively processes an input directed acyclic graph (DAG) representing an initial CNN, a set of nodes, a set of exogenous nodes, and a resolution based on the CNN. An iteration for a node may include recursively performing the iteration upon each node in a descendant node set to create a descendant DAG, and upon each node in ancestor node sets to create ancestor DAGs, the ancestor node sets being a remainder of nodes in the temporary DAG after removing nodes of the descendent node set. The descendant and ancestor DAGs are merged, and a latent layer is created that includes a latent node for each ancestor node set. Each latent node is set to be a parent of sets of parentless nodes in a combined descendant DAG and ancestors DAGs before returning.Type: GrantFiled: December 22, 2017Date of Patent: May 18, 2021Assignee: Intel CorporationInventors: Guy Koren, Raanan Yonatan Yehezkel Rohekar, Shami Nisimov, Gal Novik
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Publication number: 20200349392Abstract: A mechanism is described for facilitating the transfer of features learned by a context independent pre-trained deep neural network to a context dependent neural network. The mechanism includes extracting a feature learned by a first deep neural network (DNN) model via the framework, wherein the first DNN model is a pre-trained DNN model for computer vision to enable context-independent classification of an object within an input video frame and training, via the deep learning framework, a second DNN model for computer vision based on the extracted feature, the second DNN model an update of the first DNN model, wherein training the second DNN model includes training the second DNN model based on a dataset including context-dependent data.Type: ApplicationFiled: July 15, 2020Publication date: November 5, 2020Applicant: Intel CorporationInventor: RAANAN YONATAN YEHEZKEL ROHEKAR
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Publication number: 20200279135Abstract: A mechanism is described for facilitating on-the-fly deep learning in machine learning for autonomous machines. A method of embodiments, as described herein, includes detecting an output associated with a first deep network serving as a user-independent model associated with learning of one or more neural networks at a computing device having a processor coupled to memory. The method may further include automatically generating training data for a second deep network serving as a user-dependent model, where the training data is generated based on the output. The method may further include merging the user-independent model with the user-dependent model into a single joint model.Type: ApplicationFiled: February 6, 2020Publication date: September 3, 2020Applicant: Intel CorporationInventor: RAANAN YONATAN YEHEZKEL ROHEKAR
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Patent number: 10572773Abstract: A mechanism is described for facilitating on-the-fly deep learning in machine learning for autonomous machines. A method of embodiments, as described herein, includes detecting an output associated with a first deep network serving as a user-independent model associated with learning of one or more neural networks at a computing device having a processor coupled to memory. The method may further include automatically generating training data for a second deep network serving as a user-dependent model, where the training data is generated based on the output. The method may further include merging the user-independent model with the user-dependent model into a single joint model.Type: GrantFiled: July 26, 2017Date of Patent: February 25, 2020Assignee: INTEL CORPORATIONInventor: Raanan Yonatan Yehezkel Rohekar
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Publication number: 20190042911Abstract: A recursive method and apparatus produce a deep convolution neural network (CNN). The method iteratively processes an input directed acyclic graph (DAG) representing an initial CNN, a set of nodes, a set of exogenous nodes, and a resolution based on the CNN. An iteration for a node may include recursively performing the iteration upon each node in a descendant node set to create a descendant DAG, and upon each node in ancestor node sets to create ancestor DAGs, the ancestor node sets being a remainder of nodes in the temporary DAG after removing nodes of the descendent node set. The descendant and ancestor DAGs are merged, and a latent layer is created that includes a latent node for each ancestor node set. Each latent node is set to be a parent of sets of parentless nodes in a combined descendant DAG and ancestors DAGs before returning.Type: ApplicationFiled: December 22, 2017Publication date: February 7, 2019Inventors: Guy Koren, Raanan Yonatan Yehezkel Rohekar, Shami Nisimov, Gal Novik
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Publication number: 20190042917Abstract: Various embodiments are generally directed to techniques for determining artificial neural network topologies, such as by utilizing probabilistic graphical models, for instance. Some embodiments are particularly related to determining neural network topologies by bootstrapping a graph, such as a probabilistic graphical model, into a multi-graphical model, or graphical model tree. Various embodiments may include logic to determine a collection of sample sets from a dataset. In various such embodiments, each sample set may be drawn randomly for the dataset with replacement between drawings. In some embodiments, logic may partition a graph into multiple subgraph sets based on each of the sample sets. In several embodiments, the multiple subgraph sets may be scored, such as with Bayesian statistics, and selected amongst as part of determining a topology for a neural network.Type: ApplicationFiled: June 21, 2018Publication date: February 7, 2019Applicant: INTEL CORPORATIONInventors: Yaniv Gurwicz, Raanan Yonatan Yehezkel Rohekar, Shami Nisimov, Guy Koren, Gal Novik