公开(公告)号：US20200026981A1

公开(公告)日：2020-01-23

申请号：US16577908

申请日：2019-09-20

Applicant: HRL Laboratories, LLC

Inventor： Hao-Yuan Chang ,  Aruna Jammalamadaka ,  Nigel D. Stepp

IPC: G06N3/04 ,  G06N3/063 ,  G06N3/08

Abstract: Described is a system for computing conditional probabilities of random variables for Bayesian inference. The system implements a spiking neural network of neurons to compute the conditional probability of two random variables X and Y. The spiking neural network includes an increment path for a synaptic weight that is proportional to a product of the synaptic weight and a probability of X, a decrement path for the synaptic weight that is proportional to a probability of X, Y, and delay and spike timing dependent plasticity (STDP) parameters such that the synaptic weight increases and decreases with the same magnitude for a single firing event.

公开(公告)号：US11449735B2

公开(公告)日：2022-09-20

申请号：US16577908

申请日：2019-09-20

Applicant: HRL Laboratories, LLC

Inventor： Hao-Yuan Chang ,  Aruna Jammalamadaka ,  Nigel D. Stepp

IPC: G06N3/04 ,  G06N3/08 ,  G06N3/063

Abstract: Described is a system for computing conditional probabilities of random variables for Bayesian inference. The system implements a spiking neural network of neurons to compute the conditional probability of two random variables X and Y. The spiking neural network includes an increment path for a synaptic weight that is proportional to a product of the synaptic weight and a probability of X, a decrement path for the synaptic weight that is proportional to a probability of X, Y, and delay and spike timing dependent plasticity (STDP) parameters such that the synaptic weight increases and decreases with the same magnitude for a single firing event.

公开(公告)号：US10902115B2

公开(公告)日：2021-01-26

申请号：US16380687

申请日：2019-04-10

Applicant: HRL Laboratories, LLC

Inventor： Richard J. Patrick ,  Nigel D. Stepp ,  Vincent De Sapio ,  Jose Cruz-Albrecht ,  John Richard Haley, Jr. ,  Thomas M. Trostel

IPC: G06F21/55 ,  G06N3/08

Abstract: Described is neuromorphic system for authorized user detection. The system includes a client device comprising a plurality of sensor types providing streaming sensor data and one or more processors. The one or more processors include an input processing component and an output processing component. A neuromorphic electronic component is embedded in or on the client device for continuously monitoring the streaming sensor data and generating out-spikes based on the streaming sensor data. Further, the output processing component classifies the streaming sensor data based on the out-spikes to detect an anomalous signal and classify the anomalous signal.

公开(公告)号：US20200257943A1

公开(公告)日：2020-08-13

申请号：US16708166

申请日：2019-12-09

Applicant: HRL LABORATORIES, LLC

Inventor： David J. Huber ,  Tsai-Ching Lu ,  Nigel D. Stepp ,  Aruna Jammalamadaka ,  Hyun J. Kim ,  Samuel D. Johnson

IPC: G06K9/62 ,  G06N3/08 ,  G06N20/00 ,  G06F40/205 ,  G06F40/284 ,  G06F16/907

Abstract: A method for generating human-machine hybrid predictions of answers to forecasting problems includes: parsing text of an individual forecasting problem to identify keywords; generating machine models based on the keywords; scraping data sources based on the keywords to collect scraped data relevant to the individual forecasting problem; providing the scraped data to the machine models; receiving machine predictions of answers to the individual forecasting problem from the machine models based on the scraped data; providing, by the computer system via a user interface, the scraped data to human participants; receiving, by the computer system via the user interface, human predictions of answers to the individual forecasting problem from the human participants; aggregating the machine predictions with the human predictions to generate aggregated predictions; and generating and outputting a hybrid prediction based on the aggregated predictions.

公开(公告)号：US11625562B2

公开(公告)日：2023-04-11

申请号：US16708166

申请日：2019-12-09

Applicant: HRL LABORATORIES, LLC

Inventor： David J. Huber ,  Tsai-Ching Lu ,  Nigel D. Stepp ,  Aruna Jammalamadaka ,  Hyun J. Kim ,  Samuel D. Johnson

IPC: G06F40/166 ,  G06K9/62 ,  G06F16/907 ,  G06N20/00 ,  G06F40/284 ,  G06F40/205 ,  G06N3/08 ,  G06F16/903 ,  G06F40/40 ,  G06Q30/0202 ,  G06Q30/0251 ,  G06Q50/00

Abstract: A method for generating human-machine hybrid predictions of answers to forecasting problems includes: parsing text of an individual forecasting problem to identify keywords; generating machine models based on the keywords; scraping data sources based on the keywords to collect scraped data relevant to the individual forecasting problem; providing the scraped data to the machine models; receiving machine predictions of answers to the individual forecasting problem from the machine models based on the scraped data; providing, by the computer system via a user interface, the scraped data to human participants; receiving, by the computer system via the user interface, human predictions of answers to the individual forecasting problem from the human participants; aggregating the machine predictions with the human predictions to generate aggregated predictions; and generating and outputting a hybrid prediction based on the aggregated predictions.

公开(公告)号：US10976429B1

公开(公告)日：2021-04-13

申请号：US15784841

申请日：2017-10-16

Applicant: HRL LABORATORIES, LLC

Inventor： Qin Jiang ,  Nigel D. Stepp ,  Praveen K. Pilly ,  Jose Cruz-Albrecht

IPC: G01S13/90 ,  G06N3/063 ,  G06N3/02 ,  G06T9/00 ,  G06N20/00 ,  G06N3/04 ,  G06K9/00 ,  G01S13/89 ,  G01S7/41

Abstract: A system configured to identify a target in a synthetic aperture radar signal includes: a feature extractor configured to extract a plurality of features from the synthetic aperture radar signal; a spiking neural network configured to encode the features as a plurality of spiking signals; a readout neural layer configured to compute a signal identifier based on the spiking signals; and an output configured to output the signal identifier, the signal identifier identifying the target.

公开(公告)号：US20200026287A1

公开(公告)日：2020-01-23

申请号：US16519814

申请日：2019-07-23

Applicant: HRL Laboratories, LLC

Inventor： Qin Jiang ,  Youngkwan Cho ,  Nigel D. Stepp ,  Steven W. Skorheim ,  Vincent De Sapio ,  Praveen K. Pilly ,  Ruggero Scorcioni

IPC: G05D1/00 ,  G05B13/02 ,  G05D1/02

Abstract: Described is a system for online vehicle recognition in an autonomous driving environment. Using a learning network comprising an unsupervised learning component and a supervised learning component, images of moving vehicles extracted from videos captured in the autonomous driving environment are learned and classified. Vehicle feature data is extracted from input moving vehicle images. The extracted vehicle feature data is clustered into different vehicle classes using the unsupervised learning component. Vehicle class labels for the different vehicle classes are generated using the supervised learning component. Based on a vehicle class label for a moving vehicle in the autonomous driving environment, the system selects an action to be performed by the autonomous vehicle, and causes the selected action to be performed by the autonomous vehicle in the autonomous driving environment.

公开(公告)号：US11521053B2

公开(公告)日：2022-12-06

申请号：US16792791

申请日：2020-02-17

Applicant: HRL Laboratories, LLC

Inventor： Nigel D. Stepp ,  Aruna Jammalamadaka

IPC: G06N3/063 ,  G06N3/08 ,  G06N3/04

Abstract: Described is a system for specifying control of a device based on a Bayesian network model. The system includes a Bayesian neuromorphic compiler having a network composition module having probabilistic computation units (PCUs) arranged in a hierarchical composition containing multi-level dependencies. The Bayesian neuromorphic compiler receives a Bayesian network model as input and produces a spiking neural network topology and configuration that implements the Bayesian network model. The network composition module learns conditional probabilities of the Bayesian network model. The system computes a conditional probability and controls a device based on the computed conditional probability.

公开(公告)号：US11347221B2

公开(公告)日：2022-05-31

申请号：US16661637

申请日：2019-10-23

Applicant: HRL LABORATORIES, LLC

Inventor： Steven W. Skorheim ,  Nigel D. Stepp ,  Ruggero Scorcioni

IPC: G06N3/04 ,  G06N3/08 ,  G05D1/00 ,  G05D1/02 ,  G05D1/10

Abstract: A method of training an artificial neural network having a series of layers and at least one weight matrix encoding connection weights between neurons in successive layers. The method includes receiving, at an input layer of the series of layers, at least one input, generating, at an output layer of the series of layers, at least one output based on the at least one input, generating a reward based on a comparison of between the at least one output and a desired output, and modifying the connection weights based on the reward. Modifying the connection weights includes maintaining a sum of synaptic input weights to each neuron to be substantially constant and maintaining a sum of synaptic output weights from each neuron to be substantially constant.

公开(公告)号：US11288572B2

公开(公告)日：2022-03-29

申请号：US16294886

申请日：2019-03-06

Applicant: HRL Laboratories, LLC

Inventor： Nigel D. Stepp ,  Aruna Jammalamadaka

IPC: G06N3/063 ,  G06N3/04 ,  G06N3/08

Abstract: Described is a system for performing probabilistic computations on mobile platform sensor data. The system translates a Bayesian model representing input mobile platform sensor data to a spiking neuronal network unit that implements the Bayesian model. Using the spiking neuronal network unit, conditional probabilities are computed for the input mobile platform sensor data, where the input mobile platform sensor data is a time series of mobile platform error codes encoded as neuronal spikes. The neuronal spikes are decoded and represent a mobile platform failure mode. The system causes the mobile platform to initiate a mitigation action based on the mobile platform failure mode.