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公开(公告)号：US20210019632A1
公开(公告)日：2021-01-21
申请号：US16875852
申请日：2020-05-15
摘要： Described is a system for continual learning using experience replay. In operation, the system receives a plurality of tasks sequentially, from which a current task is fed to an encoder. The current task has data points associated with the current task. The encoder then maps the data points into an embedding space, which reflects the data points as discriminative features. A decoder then generates pseudo-data points from the discriminative features, which are provided back to the encoder. The discriminative features are updated in the embedding space based on the pseudo-data points. The encoder then learns (updates) a classification of a new task by matching the new task with the discriminative features in the embedding space.
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公开(公告)号：US11176477B2
公开(公告)日：2021-11-16
申请号：US16719668
申请日：2019-12-18
摘要： Described is a system for unsupervised domain adaptation in an autonomous learning agent. The system adapts a learned model with a set of unlabeled data from a target domain, resulting in an adapted model. The learned model was previously trained to perform a task using a set of labeled data from a source domain. The set of labeled data has a first input data distribution, and the set of unlabeled target data has a second input data distribution that is distinct from the first input data distribution. The adapted model is implemented in the autonomous learning agent, causing the autonomous learning agent to perform the task in the target domain.
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3.发明申请公开(公告)号：US20200264300A1
公开(公告)日：2020-08-20
申请号：US16752527
申请日：2020-01-24
发明人： Mohammad Rostami , Soheil Kolouri
摘要： Described is a system for transferring learned knowledge from an electro-optical (EO) domain to a synthetic-aperture-radar (SAR) domain. The system uses a measured similarity between the EO domain and the SAR domain to train a model for classifying SAR images using knowledge previously learned from the electro-optical (EO) domain. Using the trained model, a SAR image is processed to determine regions of interest in the SAR image. A region of interest is classified to determine whether the region of interest corresponds to an object of interest, and classified regions of interest that contain the object of interest are output. The object of interest is displayed on a visualization map, and the visualization map is automatically updated to reflect a change in position of the object of interest.
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公开(公告)号：US10908616B2
公开(公告)日：2021-02-02
申请号：US16033638
申请日：2018-07-12
发明人： Soheil Kolouri , Mohammad Rostami , Kyungnam Kim , Yuri Owechko
摘要： Described is a system for object recognition. The system generates a training image set of object images from multiple image classes. Using a training image set and annotated semantic attributes, a model is trained that maps visual features from known images to the annotated semantic attributes using joint sparse representations with respect to dictionaries of visual features and semantic attributes. The trained model is used for mapping visual features of an unseen input image to its semantic attributes. The unseen input image is classified as belonging to an image class, and a device is controlled based on the classification of the unseen input image.
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公开(公告)号：US11791018B1
公开(公告)日：2023-10-17
申请号：US16513592
申请日：2019-07-16
摘要： Described is a system for automatically identifying chemical properties of a molecule. A chemical representation of a molecular structure is converted into atomic features and an adjacency matrix. The atomic features and the adjacency matrix are processed with a neural network, resulting in neural activations corresponding to each atom in the molecular structure. The system determines a probability for each atom quantifying its relevance for a given chemical characteristic. The probabilities are displayed as a graphical representation on the molecular structure, and groups of atoms are identified for the given chemical characteristic from the graphical representation. The identified groups of atoms for the given chemical characteristic are stored in a database, and a new molecule having the given chemical characteristic is designed based on the stored identified groups of atoms.
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公开(公告)号：US11620527B2
公开(公告)日：2023-04-04
申请号：US16262878
申请日：2019-01-30
发明人： Zachary Murez , Soheil Kolouri , Kyungnam Kim , Mohammad Rostami
摘要： Described is a system for adapting a deep convolutional neural network (CNN). A deep CNN is first trained on an annotated source image domain. The deep CNN is adapted to a new target image domain without requiring new annotations by determining domain agnostic features that map from the annotated source image domain and a target image domain to a joint latent space, and using the domain agnostic features to map the joint latent space to annotations for the target image domain.
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公开(公告)号：US11645544B2
公开(公告)日：2023-05-09
申请号：US16875852
申请日：2020-05-15
CPC分类号： G06N3/088 , G06N3/0454
摘要： Described is a system for continual learning using experience replay. In operation, the system receives a plurality of tasks sequentially, from which a current task is fed to an encoder. The current task has data points associated with the current task. The encoder then maps the data points into an embedding space, which reflects the data points as discriminative features. A decoder then generates pseudo-data points from the discriminative features, which are provided back to the encoder. The discriminative features are updated in the embedding space based on the pseudo-data points. The encoder then learns (updates) a classification of a new task by matching the new task with the discriminative features in the embedding space.
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公开(公告)号：US11448753B2
公开(公告)日：2022-09-20
申请号：US16752527
申请日：2020-01-24
发明人： Mohammad Rostami , Soheil Kolouri
摘要： Described is a system for transferring learned knowledge from an electro-optical (EO) domain to a synthetic-aperture-radar (SAR) domain. The system uses a measured similarity between the EO domain and the SAR domain to train a model for classifying SAR images using knowledge previously learned from the electro-optical (EO) domain. Using the trained model, a SAR image is processed to determine regions of interest in the SAR image. A region of interest is classified to determine whether the region of interest corresponds to an object of interest, and classified regions of interest that contain the object of interest are output. The object of interest is displayed on a visualization map, and the visualization map is automatically updated to reflect a change in position of the object of interest.
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公开(公告)号：US20210192363A1
公开(公告)日：2021-06-24
申请号：US17066457
申请日：2020-10-08
摘要： Described is a system for continual adaptation of a machine learning model implemented in an autonomous platform. The system adapts knowledge previously learned by the machine learning model for performance in a new domain. The system receives a consecutive sequence of new domains comprising new task data. The new task data and past learned tasks are forced to share a data distribution in an embedding space, resulting in a shared generative data distribution. The shared generative data distribution is used to generate a set of pseudo-data points for the past learned tasks. Each new domain is learned using both the set of pseudo-data points and the new task data. The machine learning model is updated using both the set of pseudo-data points and the new task data.
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公开(公告)号：US20190025848A1
公开(公告)日：2019-01-24
申请号：US16033638
申请日：2018-07-12
发明人： Soheil Kolouri , Mohammad Rostami , Kyungnam Kim , Yuri Owechko
摘要： Described is a system for object recognition. The system generates a training image set of object images from multiple image classes. Using a training image set and annotated semantic attributes, a model is trained that maps visual features from known images to the annotated semantic attributes using joint sparse representations with respect to dictionaries of visual features and semantic attributes. The trained model is used for mapping visual features of an unseen input image to its semantic attributes. The unseen input image is classified as belonging to an image class, and a device is controlled based on the classification of the unseen input image.
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