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公开(公告)号：US20180081053A1
公开(公告)日：2018-03-22
申请号：US15689656
申请日：2017-08-29
Applicant: NEC Laboratories America, Inc.
Inventor： Iain Melvin , Eric Cosatto , Igor Durdanovic , Hans Peter Graf
CPC classification number: G01S13/931 , B60G2400/823 , B60Q9/008 , B60R1/00 , B60R2300/301 , B60R2300/8093 , B60W30/09 , B60W2420/42 , B60W2420/52 , G01S7/20 , G01S7/2955 , G01S7/417 , G01S13/867 , G01S17/936 , G01S2013/936 , G01S2013/9367 , G01S2013/9375 , G06K9/00805 , G06K9/46 , G06K9/6215 , G06K9/6232 , G06N3/0454 , G06N3/08 , G06N3/084
Abstract: A computer-implemented method and system are provided. The system includes an image capture device configured to capture image data relative to an ambient environment of a user. The system further includes a processor configured to detect and localize objects, in a real-world map space, from the image data using a trainable object localization Convolutional Neural Network (CNN). The CNN is trained to detect and localize the objects from image and radar pairs that include the image data and radar data for different scenes of a natural environment. The processor is further configured to perform a user-perceptible action responsive to a detection and a localization of an object in an intended path of the user.
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公开(公告)号：US20170337467A1
公开(公告)日：2017-11-23
申请号：US15590666
申请日：2017-05-09
Applicant: NEC Laboratories America, Inc.
Inventor： Asim Kadav , Igor Durdanovic , Hans Peter Graf , Hao Li
CPC classification number: G06N3/082 , G06F17/153 , G06F17/17 , G06K9/00771 , G06K9/4628 , G06K9/6228 , G06K9/627 , G06K9/6296 , G06K9/66 , G06K2009/00738 , G06N3/0427 , G06N3/0454 , G06N3/0481 , G06N5/045 , G08B13/00 , G08B29/186 , H03H2222/04
Abstract: Security systems and methods for detecting intrusion events include one or more sensors configured to monitor an environment. A pruned convolutional neural network (CNN) is configured process information from the one or more sensors to classify events in the monitored environment. CNN filters having the smallest summed weights have been pruned from the pruned CNN. An alert module is configured to detect an intrusion event in the monitored environment based on event classifications. A control module is configured to perform a security action based on the detection of an intrusion event.
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公开(公告)号：US10885437B2
公开(公告)日：2021-01-05
申请号：US15590666
申请日：2017-05-09
Applicant: NEC Laboratories America, Inc.
Inventor： Asim Kadav , Igor Durdanovic , Hans Peter Graf , Hao Li
IPC: G06N3/04 , G06N3/08 , G06K9/62 , G06K9/00 , G06K9/46 , G08B13/00 , G08B29/18 , G06F17/15 , G06F17/17 , G06K9/66 , G06N5/04
Abstract: Security systems and methods for detecting intrusion events include one or more sensors configured to monitor an environment. A pruned convolutional neural network (CNN) is configured process information from the one or more sensors to classify events in the monitored environment. CNN filters having the smallest summed weights have been pruned from the pruned CNN. An alert module is configured to detect an intrusion event in the monitored environment based on event classifications. A control module is configured to perform a security action based on the detection of an intrusion event.
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公开(公告)号：US10740676B2
公开(公告)日：2020-08-11
申请号：US15595049
申请日：2017-05-15
Applicant: NEC Laboratories America, Inc.
Inventor： Igor Durdanovic , Hans Peter Graf
Abstract: Methods and systems of training a neural network includes training a neural network based on training data. Weights of a layer of the neural network are multiplied by an attrition factor. A block of weights is pruned from the layer if the block of weights in the layer has a contribution to an output of the layer that is below a threshold.
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5.发明申请公开(公告)号：US20180336431A1
公开(公告)日：2018-11-22
申请号：US15979505
申请日：2018-05-15
Applicant: NEC Laboratories America, Inc.
Inventor： Asim Kadav , Igor Durdanovic , Hans Peter Graf
CPC classification number: G06K9/4628 , G06K9/00624 , G06K9/0063 , G06K9/00771 , G06K9/00805 , G06K9/6217 , G06K9/627 , G06K9/6288 , G06K9/66 , G06N3/04 , G06N3/0445 , G06N3/0454 , G06N3/082 , G06N5/046
Abstract: Systems and methods for predicting changes to an environment, including a plurality of remote sensors, each remote sensor being configured to capture images of an environment. A processing device is included on each remote sensor, the processing device configured to recognize and predict a change to the environment using a pruned convolutional neural network (CNN) stored on the processing device, the pruned CNN being trained to recognize features in the environment by training a CNN with a dataset and removing filters from layers of the CNN that are below a significance threshold for image recognition to produce the pruned CNN. A transmitter is configured to transmit the recognized and predicted change to a notification device such that an operator is alerted to the change.
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Patent Agency Ranking
公开(公告)号：US10832136B2
公开(公告)日：2020-11-10
申请号：US15590620
申请日：2017-05-09
Applicant: NEC Laboratories America, Inc.
Inventor： Asim Kadav , Igor Durdanovic , Hans Peter Graf , Hao Li
IPC: G06N3/08 , G06N3/04 , G06K9/62 , G06K9/00 , G06K9/46 , G08B13/00 , G08B29/18 , G06F17/15 , G06F17/17 , G06K9/66 , G06N5/04
Abstract: Methods and systems for pruning a convolutional neural network (CNN) include calculating a sum of weights for each filter in a layer of the CNN. The filters in the layer are sorted by respective sums of weights. A set of m filters with the smallest sums of weights is filtered to decrease a computational cost of operating the CNN. The pruned CNN is retrained to repair accuracy loss that results from pruning the filters.
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7.发明申请公开(公告)号：US20180336425A1
公开(公告)日：2018-11-22
申请号：US15979509
申请日：2018-05-15
Applicant: NEC Laboratories America, Inc.
Inventor： Asim Kadav , Igor Durdanovic , Hans Peter Graf
CPC classification number: G06K9/4628 , G06K9/00624 , G06K9/0063 , G06K9/00771 , G06K9/00805 , G06K9/6217 , G06K9/627 , G06K9/6288 , G06K9/66 , G06N3/04 , G06N3/0445 , G06N3/0454 , G06N3/082 , G06N5/046
Abstract: Systems and methods for surveillance are described, including an image capture device configured to mounted to an autonomous vehicle, the image capture device including an image sensor. A storage device is included in communication with the processing system, the storage device including a pruned convolutional neural network (CNN) being trained to recognize obstacles in a road according to images captured by the image sensor by training a CNN with a dataset and removing filters from layers of the CNN that are below a significance threshold for image recognition to produce the pruned CNN. A processing device is configured to recognize the obstacles by analyzing the images captured by the image sensor with the pruned CNN and to predict movement of the obstacles such that the autonomous vehicle automatically and proactively avoids the obstacle according to the recognized obstacle and predicted movement.
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公开(公告)号：US20170337472A1
公开(公告)日：2017-11-23
申请号：US15595049
申请日：2017-05-15
Applicant: NEC Laboratories America, Inc.
Inventor： Igor Durdanovic , Hans Peter Graf
CPC classification number: G06N3/082 , G06N3/0454
Abstract: Methods and systems of training a neural network includes training a neural network based on training data. Weights of a layer of the neural network are multiplied by an attrition factor. A block of weights is pruned from the layer if the block of weights in the layer has a contribution to an output of the layer that is below a threshold.
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公开(公告)号：US20210319847A1
公开(公告)日：2021-10-14
申请号：US17197166
申请日：2021-03-10
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Wenchao Yu , Hans Peter Graf , Igor Durdanovic
Abstract: A method is provided for peptide-based vaccine generation. The method receives a dataset of positive and negative binding peptide sequences. The method pre-trains a set of peptide binding property predictors on the dataset to generate training data. The method trains a Wasserstein Generative Adversarial Network (WGAN) only on the positive binding peptide sequences, in which a discriminator of the WGAN is updated to distinguish generated peptide sequences from sampled positive peptide sequences from the training data, and a generator of the WGAN is updated to fool the discriminator. The method trains the WGAN only on the positive binding peptide sequences while simultaneously updating the generator to minimize a kernel Maximum Mean Discrepancy (MMD) loss between the generated peptide sequences and the sampled peptide sequences and maximize prediction accuracies of a set of pre-trained peptide binding property predictors with parameters of the set of pre-trained peptide binding property predictors being fixed.
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10.发明申请公开(公告)号：US20190122111A1
公开(公告)日：2019-04-25
申请号：US16168244
申请日：2018-10-23
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Bing Bai , Alexandru Niculescu-Mizil , Igor Durdanovic , Hans Peter Graf
Abstract: Systems and methods for predicting new relationships in the knowledge graph, including embedding a partial triplet including a head entity description and a relationship or a tail entity description to produce a separate vector for each of the head, relationship, and tail. The vectors for the head entity, relationship, and tail entity can be combined into a first matrix, and adaptive kernels generated from the entity descriptions can be applied to the matrix through convolutions to produce a second matrix having a different dimension from the first matrix. An activation function can be applied to the second matrix to obtain non-negative feature maps, and max-pooling can be used over the feature maps to get subsamples. A fixed length vector, Z, flattens the subsampling feature maps into a feature vector, and a linear mapping method is used to map the feature vectors into a prediction score.
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