Patents by Inventor Bradley J. Baker
Bradley J. Baker 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: 11948063Abstract: Computer systems and computer-implemented methods improve a base neural network. In an initial training, preliminary activations values computed for base network nodes for data in the training data set are stored in memory. After the initial training, a new node set is merged into the base neural network to form an expanded neural network, including directly connecting each of the nodes of the new node set to one or more base network nodes. Then the expanded neural network is trained on the training data set using a network error loss function for the expanded neural network.Type: GrantFiled: June 1, 2023Date of Patent: April 2, 2024Assignee: D5AI LLCInventors: James K. Baker, Bradley J. Baker
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Patent number: 11836600Abstract: Computer systems and computer-implemented methods train a neural network, by: (a) computing for each datum in a set of training data, activation values for nodes in the neural network and estimates of partial derivatives of an objective function for the neural network for the nodes in the neural network; (b) selecting a target node of the neural network and/or a target datum in the set of training data; (c) selecting a target-specific improvement model for the neural network, wherein the target-specific improvement model, when added to the neural network, improves performance of the neural network for the target node and/or the target datum, as the case may be; (d) training the target-specific improvement model; (e) merging the target-specific improvement model with the neural network to form an expanded neural network; and (f) training the expanded neural network.Type: GrantFiled: July 28, 2021Date of Patent: December 5, 2023Assignee: D5AI LLCInventors: James K. Baker, Bradley J. Baker
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Publication number: 20230385608Abstract: Computer systems and computer-implemented methods train a neural network, by: (a) computing for each datum in a set of training data, activation values for nodes in the neural network and estimates of partial derivatives of an objective function for the neural network for the nodes in the neural network; (b) selecting a target node of the neural network and/or a target datum in the set of training data; (c) selecting a target-specific improvement model for the neural network, wherein the target-specific improvement model, when added to the neural network, improves performance of the neural network for the target node and/or the target datum, as the case may be; (d) training the target-specific improvement model; (e)merging the target-specific improvement model with the neural network to form an expanded neural network; and (f) training the expanded neural network.Type: ApplicationFiled: June 1, 2023Publication date: November 30, 2023Applicant: D5AI LLCInventors: James K. BAKER, Bradley J. BAKER
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Publication number: 20230368029Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.Type: ApplicationFiled: July 13, 2023Publication date: November 16, 2023Applicant: D5AI LLCInventors: James K. BAKER, Bradley J. BAKER
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Publication number: 20230359860Abstract: Data-dependent node-to-node knowledge sharing to increase the interpretability of the activation pattern of one or more nodes in a neural network, is implemented by a set of knowledge sharing links. Each link may comprise a knowledge providing node or other source P and a knowledge receiving node R. A knowledge sharing link can impose a node-specific regularization on the knowledge receiving node R to help guide the knowledge receiving node R to have an activation pattern that is more easily interpreted. The specification and training of the knowledge sharing links may be controlled by a cooperative human-AI learning supervisor system in which a human and an artificial intelligence system work cooperatively to improve the interpretability and performance of the client system.Type: ApplicationFiled: July 17, 2023Publication date: November 9, 2023Applicant: D5AI LLCInventors: James K. BAKER, Bradley J. BAKER
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Patent number: 11748624Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.Type: GrantFiled: July 15, 2020Date of Patent: September 5, 2023Assignee: D5AI LLCInventors: James K. Baker, Bradley J. Baker
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Patent number: 11741340Abstract: Data-dependent node-to-node knowledge sharing to increase the interpretability of the activation pattern of one or more nodes in a neural network, is implemented by a set of knowledge sharing links. Each link may comprise a knowledge providing node or other source P and a knowledge receiving node R. A knowledge sharing link can impose a node-specific regularization on the knowledge receiving node R to help guide the knowledge receiving node R to have an activation pattern that is more easily interpreted. The specification and training of the knowledge sharing links may be controlled by a cooperative human-AI learning supervisor system in which a human and an artificial intelligence system work cooperatively to improve the interpretability and performance of the client system.Type: GrantFiled: April 13, 2020Date of Patent: August 29, 2023Assignee: D5AI LLCInventors: James K. Baker, Bradley J. Baker
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Publication number: 20230072844Abstract: Data-dependent node-to-node knowledge sharing to increase the interpretability of the activation pattern of one or more nodes in a neural network, is implemented by a set of knowledge sharing links. Each link may comprise a knowledge providing node or other source P and a knowledge receiving node R. A knowledge sharing link can impose a node-specific regularization on the knowledge receiving node R to help guide the knowledge receiving node R to have an activation pattern that is more easily interpreted. The specification and training of the knowledge sharing links may be controlled by a cooperative human-AI learning supervisor system in which a human and an artificial intelligence system work cooperatively to improve the interpretability and performance of the client system.Type: ApplicationFiled: April 13, 2020Publication date: March 9, 2023Applicant: D5AI LLCInventors: James K. BAKER, Bradley J. BAKER
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Patent number: 11461655Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.Type: GrantFiled: January 28, 2019Date of Patent: October 4, 2022Assignee: D5AI LLCInventors: James K. Baker, Bradley J. Baker
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Patent number: 11321612Abstract: Computer-implemented systems and methods soft-tie learned parameters of a neural network(s). The soft-tying comprises: applying a common label to the first and second learned parameters; and as part of the training, and in response to the first and second learned parameters having the common label, applying a regularization penalty to a loss function for the first learned parameter upon a determination that the first learned parameter is different than the second learned parameter. The learned parameters can be connection weights, node biases, and/or parametric model statistics. The application of the regularization penalty can be influenced by a soft-tying hyperparameter.Type: GrantFiled: October 12, 2020Date of Patent: May 3, 2022Assignee: D5AI LLCInventors: James K. Baker, Bradley J. Baker
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Publication number: 20220058467Abstract: Computer systems and computer-implemented methods train a neural network, by: (a) computing for each datum in a set of training data, activation values for nodes in the neural network and estimates of partial derivatives of an objective function for the neural network for the nodes in the neural network; (b) selecting a target node of the neural network and/or a target datum in the set of training data; (c) selecting a target-specific improvement model for the neural network, wherein the target-specific improvement model, when added to the neural network, improves performance of the neural network for the target node and/or the target datum, as the case may be; (d) training the target-specific improvement model; (e) merging the target-specific improvement model with the neural network to form an expanded neural network; and (f) training the expanded neural network.Type: ApplicationFiled: July 28, 2021Publication date: February 24, 2022Inventors: James K. Baker, Bradley J. Baker
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Patent number: 11195097Abstract: Computer-implemented systems and methods build ensembles for deep learning through parallel data splitting by creating and training an ensemble of up to 2n ensemble members based on a single base network and a selection of n network elements. The ensemble members are created by the “blasting” process, in which training data are selected for each of the up to 2n ensemble members such that each of the ensemble members trains with updates in a different direction from each of the other ensemble members. The ensemble members may also be trained with joint optimization.Type: GrantFiled: July 2, 2019Date of Patent: December 7, 2021Assignee: D5AI LLCInventors: James K. Baker, Bradley J. Baker
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Patent number: 11151455Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.Type: GrantFiled: July 7, 2020Date of Patent: October 19, 2021Assignee: D5AI LLCInventors: James K. Baker, Bradley J. Baker
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Patent number: 11093830Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.Type: GrantFiled: June 25, 2020Date of Patent: August 17, 2021Assignee: D5AI LLCInventors: James K. Baker, Bradley J. Baker
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Publication number: 20210248474Abstract: Computer-implemented systems and methods build ensembles for deep learning through parallel data splitting by creating and training an ensemble of up to 2n ensemble members based on a single base network and a selection of n network elements. The ensemble members are created by the “blasting” process, in which training data are selected for each of the up to 2n ensemble members such that each of the ensemble members trains with updates in a different direction from each of the other ensemble members. The ensemble members may also be trained with joint optimization.Type: ApplicationFiled: July 2, 2019Publication date: August 12, 2021Inventors: James K. Baker, Bradley J. Baker
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Patent number: 11087217Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.Type: GrantFiled: July 8, 2020Date of Patent: August 10, 2021Assignee: D5AI LLCInventors: James K. Baker, Bradley J. Baker
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Patent number: 11074506Abstract: Computer systems and computer-implemented methods train a machine-learning regression system. The method comprises the step of generating, with a machine-learning generator, output patterns; distorting the output patterns of the generator by a scale factor to generate distorted output patterns; and training the machine-learning regression system to predict the scaling factor, where the regression system receives the distorted output patterns as input and learns and the scaling factor is a target value for the regression system. The method may further comprise, after training the machine-learning regression system, training a second machine-learning generator by back propagating partial derivatives of an error cost function from the regression system to the second machine-learning generator and training the second machine-learning generator using stochastic gradient descent.Type: GrantFiled: September 17, 2018Date of Patent: July 27, 2021Assignee: D5AI LLCInventors: James K. Baker, Bradley J. Baker
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Patent number: 11010671Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.Type: GrantFiled: July 15, 2020Date of Patent: May 18, 2021Assignee: D5AI LLCInventors: James K. Baker, Bradley J. Baker
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Publication number: 20210056381Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.Type: ApplicationFiled: July 8, 2020Publication date: February 25, 2021Inventors: James K. Baker, Bradley J. Baker
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Publication number: 20210056380Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.Type: ApplicationFiled: July 7, 2020Publication date: February 25, 2021Inventors: James K. Baker, Bradley J. Baker