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公开(公告)号：US10915711B2
公开(公告)日：2021-02-09
申请号：US16214082
申请日：2018-12-09
发明人： Einat Kermany , Guy Hadash , George Kour , Ofer Lavi , Boaz Carmeli
摘要： In some examples, a system for executing natural language processing techniques can include a processor to detect text comprising a word and a number. The processor can also embed, via a word embedding model, the word into a first vector of a vector space and embed the number by converting the number into a second vector of the vector space. Additionally, the processor can train a deep neural network to execute instructions based on the first embedded vector of the word and the second embedded vector of the number. Furthermore, the processor can process an instruction based on the trained deep neural network.
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公开(公告)号：US11625609B2
公开(公告)日：2023-04-11
申请号：US16008058
申请日：2018-06-14
发明人： Boaz Carmeli , Guy Hadash , Einat Kermany , Ofer Lavi , Guy Lev , Oren Sar-Shalom
摘要： During end-to-end training of a Deep Neural Network (DNN), a differentiable estimator subnetwork is operated to estimate a functionality of an external software application. Then, during inference by the trained DNN, the differentiable estimator subnetwork is replaced with the functionality of the external software application, by enabling API communication between the DNN and the external software application.
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公开(公告)号：US11928556B2
公开(公告)日：2024-03-12
申请号：US16236402
申请日：2018-12-29
发明人： Guy Hadash , Boaz Carmeli , George Kour
IPC分类号： G06N20/00
CPC分类号： G06N20/00
摘要： Methods and systems for a reinforcement learning system. A spatial and temporal representation of an observed state of an environment is encoded. A previous state is estimated from a given state and a size of a reward is adjusted based on a difference between the estimated previous state and the previous state.
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公开(公告)号：US20200184015A1
公开(公告)日：2020-06-11
申请号：US16214082
申请日：2018-12-09
发明人： Einat Kermany , Guy Hadash , George Khor , Ofer Lavi , Boaz Carmeli
摘要： In some examples, a system for executing natural language processing techniques can include a processor to detect text comprising a word and a number. The processor can also embed, via a word embedding model, the word into a first vector of a vector space and embed the number by converting the number into a second vector of the vector space. Additionally, the processor can train a deep neural network to execute instructions based on the first embedded vector of the word and the second embedded vector of the number. Furthermore, the processor can process an instruction based on the trained deep neural network.
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公开(公告)号：US11790239B2
公开(公告)日：2023-10-17
申请号：US16236428
申请日：2018-12-29
发明人： George Kour , Guy Hadash , Yftah Ziser , Ofer Lavi , Guy Lev
CPC分类号： G06N3/088 , G05D1/0088 , G05D1/021 , G05D1/101 , G06F18/217
摘要： A specification of a property required to be upheld by a computerized machine learning system is obtained. A training data set corresponding to the property and inputs and outputs of the system is built. The system is trained on the training data set. Activity of the system is monitored before, during, and after the training. Based on the monitoring, performance of the system is evaluated to determine whether the system, once trained on the training data set, upholds the property.
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公开(公告)号：US20190385060A1
公开(公告)日：2019-12-19
申请号：US16008058
申请日：2018-06-14
发明人： BOAZ CARMELI , Guy Hadash , Einat Kermany , Ofer Lavi , Guy Lev , Oren Sar-Shalom
摘要： During end-to-end training of a Deep Neural Network (DNN), a differentiable estimator subnetwork is operated to estimate a functionality of an external software application. Then, during inference by the trained DNN, the differentiable estimator subnetwork is replaced with the functionality of the external software application, by enabling API communication between the DNN and the external software application.
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